Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes
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AI technologies are about to have a huge impact on the mortgage market… In November last year the founders of Revolut announced plans to launch a “fully digital, instant” mortgage in Lithuania and Ireland in 2025. Details were sketchy but the company said that mortgages will be part of a “comprehensive credit offering” it intends to build.
Neobanking progress with AI
Digital only banks, like Revolut and Monzo, are renowned for using the power of technology and data science to create efficiencies and improve customer experience. The reason neobanks have been so successful is because they provide a modern, convenient and cost-effective alternative to traditional banking. This is done a transparent way, through fast onboarding, 24/7 app access and instant notifications. All with a user-friendly interface.
While many financial services sectors have embraced financial technology in the way Revolut and Monzo have for the retail banking sector, the mortgage sector has struggled to make a real breakthrough here. Why hasn’t the mortgage industry caught up one might ask? Mortgages are complex financial products, existing at the intersection of justifiably stringent regulation. They represent the single biggest financial commitment people make in their lifetimes. Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.
Disrupting the Mortgage Market
The mortgage industry is one financial services sector that has been yearning to be shaken up by the FinTech industry for some time. While it’s encouraging to see a successful brand like Revolut enter this market, what is less known is that huge progress is being made already by smaller and less well known FinTech disruptors.
For example, the mortgage technology company MQube has developed a “new fast way” of delivering mortgage offers using the cutting edge of AI technology and data science. Today, it still typically takes several weeks to get a confirmed mortgage offer. This is one of the major reasons the homebuying process can be so time consuming and stressful for brokers and borrowers. The mortgage process is characterised by bureaucracy, paperwork, delays and often frustratingly opaque decision-making by lenders. This leads to stress and uncertainty for consumers, and their advisors. And at a time when they have plenty of other property-purchase related challenges to contend with.
Our proprietary research shows us, and this will come as no surprise, that the biggest pain point for borrowers and brokers about the mortgage process is that it is time consuming, paperwork heavy and stressful. Imagine a world where getting a mortgage is as quick and as easy as getting car insurance. This is MQube’s vision.
MQube – AI-powered Mortgages
MQube‘s AI-powered mortgage origination platform allows mortgage lenders to fully underwrite loan applications in minutes. MPowered Mortgages is MQube’s lending arm and competes for residential business alongside the big banks. It uses MQube’s AI-driven mortgage origination platform and is now able to offer a lending decision within one working day to 96% of completed applications.
The platform leverages state-of-the-art artificial intelligence and machine learning to assess around 20,000 data points in real-time. This enables lenders to process mortgage applications in minutes, transforming the industry standard of days or weeks. It automates the entire underwriting journey, from application to completion. This helps to provide a faster service, reduce costs, mitigate risks, and to make strategic adjustments quickly and effectively. By assessing documents and data in real-time during the application, it is able to build a clearer and deeper understanding of a consumers’ circumstances and specific needs. Applicants are never asked questions when MQube can independently source and verify that data, leading to a streamlined and paperless experience. Furthermore, this whole process reduces dependency on human intervention.
The benefits of AI
More and more lenders are seeing the benefits AI and financial technology can bring to their business. They are beginning to adopt such AI-driven financial systems which are scalable and serve to address systemic problems in this industry. The mortgage industry is still some way behind the neobanks, but what’s hugely exciting to see is the progress that has been made so far. Moreover, if FinTechs continue to innovate this sector and if lenders continue to embrace financial technology and use at scale, then getting a mortgage could genuinely become a quick, easy and stress free process. At this point, the mortgage industry could begin to see a shift in consumer perception and change in consumer behaviour. A new frontier for the mortgage industry is upon us.
Fernando Henrique Silva, SVP Digital Solutions, EMEA at CI&T, on how finance firms can best leverage AI to unlock bespoke services and rapid issue resolution
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When OpenAI released ChatGPT in November 2022, businesses in banking and finance quickly recognised the commercial potential of Generative AI (GenAI). However, due to the AI technology’s nascent qualities, archaic legacy systems and a lack of established strategies for integration, leaders have struggled to translate GenAI into greater revenues.
Two years on, the landscape is finally taking shape. According to PwC, 70% of global CEOs now expect GenAI to significantly reshape how their operations create value. Furthermore, more than two-thirds are already working with AI, having reworked their tech strategies to align with AI-driven opportunities.
Of course, the banking and finance sector is no stranger to technological change. The first plastic credit card was introduced in 1959, by American Express. The ATM was launched in London by Barclays Bank. And today, mobile banking, investing and high-level financial management can be done by any smart device nestled in a person’s pocket.
However, as with any new frontier tech, GenAI has its risks: implementation challenges, upskilling, regulatory and ethical considerations—these risks are heightened in finance and banking. And there’s the classic possibility of simply getting it wrong. Plus, what’s hot in GenAI today may be old news tomorrow.
To help organisations drive change within, let’s explore the good, the bad, and the ugly of GenAI adoption through the lens of recent insights from CI&T research and case studies.
The Good side of GenAI
The analogy between the Old West and GenAI holds up: both involve exploring new territories, uncovering valuable resources, and building infrastructure. Today, these frontier outposts are becoming cities, and full-scale reinvention is on the horizon for financial institutions.
So, what’s the new gold rush? According to CI&T’s new research, The Future of Finance: How AI is powering the intelligence era, the answer is ‘hyper-personalisation.’ This field is ripe, with fintech firms using it to deliver two key benefits: bespoke services and rapid issue resolution.
Using Customer Data Profile software—tools that gather and standardise data from online and offline sources to create detailed customer profiles—GenAI is helping these firms take personalisation to new depths. This can enable bespoke services in real-time. Indeed, McKinsey reports that personalisation drives profit: companies that prioritise it achieve growth rates 40% higher than their peers. For example, it enables institutions to offer solutions that foster smarter money habits among customers. This can be done by aligning services with consumption patterns and inflationary trends. This strengthens customer loyalty while driving cross-selling opportunities. Similarly, by facilitating enhanced financial decision-making, financial institutions can provide tailored advice and tools that differentiate their services in a competitive market, boosting retention rates.
On the investment side, hyper-personalisation creates avenues for smart investment moves by delivering customised strategies aligned with individual risk profiles. This not only attracts more customers but also improves portfolio performance, translating into increased asset management fees and long-term profitability.
GenAI is also giving businesses the gift of time. By 2030, up to 30% of current hours worked could be automated. For example, in the financial sector, portfolio managers are using GenAI to automate routine performance and risk reports. Hyper-personalisation could lead to strategies tailored to individual risk appetites, the latter being a revenue opportunity.
The Bad with GenAI
GenAI is like the newest member of the crew, full of promise but with some questionable traits. Without oversight, it can enable manipulation, misinformation, and privacy breaches. The tech, unmanaged, can also be prone to biases and inaccuracies. Often presenting errors as facts, adding pressure on teams to manage them. Moreover, it poses a security risk, requiring businesses to safeguard their data, or risk being ‘robbed in the night.’
To manage these risks, GenAI is increasingly subject to complex regulations. Gartner predicts that by 2026, 50% of governments will introduce regulations and policies to enforce the responsible use of AI. These challenges will be even more significant in banking and finance.
Balancing the pros and cons of GenAI is the key to extracting value. GenAI itself can often help. For example, CI&T assisted fintech firm Bulla, which specialises in flexible credit and benefits, with managing common complaints. Using our enterprise-ready GenAI platform, CI&T FLOW, Bulla analysed customer service data to gain a detailed view of pain points and rethink support systems. They also used it to give employees access to essential information and to train staff in GenAI.
The Ugly side of Artificial Intelligence
When the going gets tough, our relationship with GenAI can take an ugly turn if outdated legacy systems stand in the way. The challenge of digging through impenetrable layers, reworking outdated processes, extracting valuable data, and training staff accustomed to old ways of working is no easy feat. Moreover, the costs can quickly add up.
Historically, banking has been one of the sectors worst affected by legacy hardware. Nearly six in ten bankers see their legacy systems as a major business challenge, describing them as a ‘spaghetti junction’ of interconnected but antiquated technologies. So, much like digging through rock in search of gold, the rigid hardware architectures designed for specific banking functions—based on old programming languages and databases—are holding back innovation. In fact, 60% of executives cite skills gaps as an obstacle to overcome in their digital transformations.
The banking sector may be on the brink of a breakthrough. We’re starting to see more AI-driven chatbots, fraud prevention, and the speeding up of time-consuming tasks such as developing code and summarising reports. However, it’s updating the legacy hardware where the real commercial value lies.
Ironically, GenAI holds the key. For one of CI&T’s leading clients, a large global bank based in South America, CI&T FLOW was able to modernise its systems by supporting the transition from COBOL to Python using a code refiner. This resulted in accelerated developer delivery, a 54% lead time reduction, and a 33% improvement in user story quality. Highlighting the power of strategically harnessing the technology. The challenge is also the solution.
As the world of GenAI transitions from Wild West to civilised modernity, businesses are going to have to get smart about how they look for commercial value. Often, the solution lies in GenAI itself. So, get started, and get started now. And in the immortal words of Clint Eastwood’s Blondie: “Two hundred thousand dollars is a lot of money. We’re gonna have to earn it.”
To learn more about how CI&T can help your business commercialise GenAI, download The Future of Finance: How AI is powering the intelligence era here.
Glenn Fratangelo, Head of Fraud Product Marketing & Strategy at NICE Actimize, on financial services fraud prevention in 2025.
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2024 marked a turning point in financial crime management with the advent of Generative AI (GenAI). McKinsey estimates GenAI could add a staggering $200-340 billion in annual value to the global banking sector. A potential revenue boost of 2.8 to 4.7%. This underscores the transformative potential of GenAI. IT IS rapidly evolving from a futuristic concept to a powerful tool in the fight against financial crime. However, 2024 was just the prelude. 2025 promises to be the year GenAI truly comes into its own. Unlocking transformative capabilities in combating increasingly sophisticated threats.
This evolution is not merely desirable, it is essential. The Office of National Statistics (ONS) reported a concerning 19% year-over-year increase in UK consumer and retail fraud incidents in 2024, reaching approximately 3.6 million. This stark reality underscores the urgent need for financial institutions (FIs) and banks to bolster their defences against financial crime. In 2025, leveraging the power of GenAI is no longer a luxury, but a necessity for protecting customers and safeguarding the financial ecosystem.
The evolving GenAI-powered fraud landscape
Fraudsters have embraced GenAI as a potent weapon in their arsenal. This technology’s ability to create realistic fakes, automate attacks and mimic customers creates a significant threat to the financial landscape.
Deepfake technology has become a particularly insidious tool. By generating highly realistic voice and facial fakes, fraudsters can bypass remote verification processes with ease. This opens doors to unauthorised access to sensitive information, enabling account takeovers and other fraudulent activities.
In addition, the rise of synthetic identities further complicates the challenge. By blending real and fabricated data, fraudsters can create personas that seamlessly infiltrate legitimate customer profiles. These synthetic identities are extremely difficult to detect, as they appear indistinguishable from genuine customers. Making it challenging for institutions to differentiate between legitimate and fraudulent activities.
Phishing scams have also undergone a dramatic evolution, becoming more sophisticated and personalised. AI-driven techniques allow fraudsters to craft personalised, convincing emails that mimic legitimate communications, resulting in significant data breaches.
Harnessing GenAI
GenAI is being used by criminals – presenting a significant challenge in the realm of fraud. It requires advanced AI capabilities such as real-time behavior analytics that use machine learning to continuously analyse all entity interaction and transaction patterns. This can identify subtle deviations from a customer’s typical behaviour. It allows for initiative-taking and the flagging of suspicious activity before any damage occurs. Moreover, providing a significant advantage over traditional, rigid rule-based systems that often fail to detect nuanced threats.
Fraud simulation and stress testing using GenAI can also empower institutions to proactively assess the resilience of their systems. By simulating potential fraud scenarios, financial institutions can identify vulnerabilities and train detection models to recognise emerging tactics. Furthermore, this proactive preparation ensures that defences remain ahead of fraudsters’ evolving methods, creating a more robust and adaptable security infrastructure.
Low volume high value fraud, such as BEC or other large value account to account transfers usually lack the quantity of data needed to optimise models. GenAI can address this by creating synthetic data that mimics real-world scenarios. This approach significantly improves the accuracy and robustness of detection models, making them more effective against new and unforeseen threats.
GenAI has the potential to transform the investigation process by automating tasks such as generating alerts and case summaries, as well as SAR narratives. This automation not only minimises errors but also frees analysts from mundane tasks, allowing them to focus on higher-value activities. The result is a significantly accelerated financial crime investigation process, enabling institutions to respond to threats with greater speed and efficiency.
The battle against fraud in 2025 and beyond
The battle against financial fraud in 2025 and beyond is an undeniable arms race. Fraudsters, wielding generative AI as their weapon, will relentlessly seek to exploit vulnerabilities. To counter this evolving threat, financial institutions must embrace AI to outmanoeuvre fraudsters and proactively protect their customers.
The future of fraud and financial crime prevention hinges on our ability to innovate and adapt. Institutions that view GenAI not just as a challenge, but as an opportunity, will emerge as leaders in this fight. AI is a force multiplier for institutions striving to combat fraud and financial crime, empowering them with smarter, faster, and more adaptive defences, we can create a more secure and trustworthy financial ecosystem. The choice to innovate in the face of adversity will define the path forward and shape the future.
FICO’s use of Blockchain for AI model governance wins Tech of the Future: Blockchain and Tokenisation award
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Global analytics software leader FICO has won the Tech of the Future – Blockchain and Tokenisation award. The Banking Tech Awards in London recognised FICO for its innovative work using Blockchain technology for AI model governance. FICO’s use of blockchain to advance responsible AI is the first time blockchain has been used to track end-to-end provenance of a machine learning model. This approach can help meet responsible AI and regulatory requirements.
FICO’s AI Innovation and Development team has developed and patented an immutable blockchain ledger. It tracks end-to-end provenance of the development, operationalisation and monitoring of machine learning models. The technology enforces the use of a corporate-wide responsible AI model development standard by organisations. It demonstrates adherence to the standard with specific requirements, people, results, testing, approvals and revisions. In addition to the Banking Tech award, Global Finance recognised FICO’s blockchain for AI technology with The Innovators award last year.
Responsible AI
“The rapid growth of AI use has made Responsible AI an imperative,” commented Dr. Scott Zoldi, chief analytics officer at FICO. “FICO is focused on technologies that ensure AI is used in an ethical way, and governance is absolutely critical. We are proud to receive another award for our groundbreaking work in this area.”
FICO is well-known as a leader in AI for financial services. Its FICO® Falcon® Fraud Manager solution, launched in 1992, was the first fraud solution to use neural networks. Today it manages some four billion payment cards worldwide. FICO has built advanced analytics capabilities into FICO® Platform, an applied intelligence platform for building decision management solutions.
Adam Zoucha, MD EMEA at FloQast, on how businesses will modernise financial processes in 2025
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With 45% of accountancy firms and in-house finance teams facing talent shortages, 2025 is going to be a critical year for many. Financial transformation is going to be the watchword. The conditions companies are facing will push them to speed up the transformation of their operations, modernising their financial processes while strengthening their company culture and vision.
The year ahead will likely see a continuation of the current period of instability, posing serious challenges for accounting teams looking to grow their business. The impact of global geopolitics is hard to predict which, twinned with the UK economy’s persistently slow growth rate, means companies will need to innovate to succeed – embracing automation, AI, and cutting-edge compliance processes.
It’s not all about the macro trends, though. On an individual level, our research this year has shown that employees are feeling the strain, and business leaders will need to take that seriously in 2025. The talent shortage is a vicious cycle – the harder it is for companies to find and retain talent, the more pressure remaining team members end up having to shoulder. The right technology can play a crucial role in reducing that stress and breaking the cycle.
Alongside those real challenges, there are real opportunities. The accounting business is changing fast, and it’s a great time to be in the industry. As we draw 2024 to a close, here are five key things accounting firms can expect to see in the new year.
Financial Transformation moving up the agenda
We’ve already looked at some of the reasons why financial transformation is going to be critical in 2025, but that doesn’t mean every CFO and accountant in the business is rushing to deliver. Based on our research 60% of accountants and CFOs still do not consider it a top priority – mainly because most don’t truly know what it means for their business, so education is key.
In essence, companies should aim to align their finance functions more closely with their organisational goals, enabling accountants to bring their expertise and insight to the decision-making process. As the finance function’s strategic role grows, there will be an urgent need for agile, digital tools that enhance collaboration and efficiency. For CFOs, embracing this transformation is essential to navigate new complexities with precision and effectiveness.
Accountancy teams will embrace new tools for the future
The talent gap present in the industry is unlikely to change any time soon. It takes time to train people, and accounting has a bit of a PR problem – its status as a secure, skilled job is battling with perceptions of stress and burnout.
As a result, in 2025, leaders will increasingly look to keep accountants motivated, engaged, and fulfilled as the declining population of new candidates continues to heap pressure on accounting teams—a trend that’s unlikely to reverse anytime soon.
It’s essential that business leaders retain their finance professionals by fostering a fulfilling work environment. They can help by upskilling accountants and adopting technologies to reduce mundane and repetitive tasks. CFOs can play a key role by equipping their teams with future-focused skills, blending technology with strategic insight to drive real value within their organisations.
AI will power Tansformation in 2025
Transformation in 2025 won’t be limited to removing internal silos and improving staff retention, crucial though those things are. We’re also going to see AI helping accountants become key players in driving business success. The real value of AI will become apparent this year. For finance teams, it will act as a copilot, automating routine tasks and giving time back to accountants to become strategic assets for their organisations.
This shift will help the industry tackle talent shortages with agility, turning challenges into opportunities for growth. Embracing AI isn’t just about keeping pace; it’s about unlocking accountants’ full potential as key players in driving business success.
Compliance will become a value-generating asset rather than a tick-box exercise
Compliance and risk, when managed properly, can drive real value for organisations. In 2025, the nuanced relationship between compliance, reputation, and risk means it’s likely to move up the corporate agenda.
Technology can be a real driver here, and compliance strategies are fundamental to the larger accounting transformation journey. By taking a more holistic approach to compliance, rather than treating it as a mere check-box exercise, compliance can become a valuable asset. Currently, only 16% of organisations take this strategic view, revealing a significant opportunity for those willing to innovate and elevate their compliance efforts.
Overall, accounting businesses may be facing rough seas, but with the right tools and investments in place, they can unlock new value in 2025: transforming financial processes, improving employee satisfaction, and stepping further into their growing role as strategic advisors.
Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking
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The banking sector stands at the crossroads of technological innovation and operational transformation. AI is taking centre stage in reshaping how financial institutions operate. The banking sector is beginning to recognise AI’s potential. It can address challenges, enhance operational efficiency, and deliver more personalised customer experiences.
The Current State of AI in Banking
Research reveals that while a number of banking organisations have yet to fully integrate AI into their operations, key areas such as debt recovery are leading the charge. The slower pace of adoption can be attributed to the highly regulated environment of banking. Because transparency, compliance, and customer trust are non-negotiable. However, despite this cautious approach, banks that have implemented artificial intelligence are already seeing significant benefits, particularly in risk management.
AI’s Role in Risk Management
Effective risk management is a cornerstone of the banking sector. AI is proving to be a powerful tool in this area. By analysing vast amounts of data and providing predictive insights, AI enables banks to mitigate risks early. They can strengthen customer portfolio stability, and make data-driven lending decisions. These capabilities are essential in a landscape where financial risks can escalate rapidly.
Beyond the expected benefits, banks have also reported enhanced customer insights as an unexpected advantage. By leveraging AI to analyse customer behaviours and preferences, banks can tailor their products and services more effectively. Furthermore, they can improve customer satisfaction and experience, whilst fostering long-term loyalty.
Challenges to Adoption
Although organisations are experiencing a multitude of advantages, the integration of AI in banking is not without its hurdles. Legacy IT systems, stringent regulatory requirements, and concerns around data privacy pose significant challenges to widespread adoption. Banks must ensure AI-driven decision-making processes are effective. Moreover, they must also be fully transparent and compliant with industry regulations. Further highlighting the importance of a gradual, strategic approach to AI implementation.
Opportunities Ahead
The potential for AI in banking extends far beyond risk management. From streamlining operational workflows to enhancing customer personalisation and improving decision-making. AI is set to drive innovation across the sector. For example, AI-powered chatbots and virtual assistants transform customer service by providing instant, 24/7 support. They can handle complex interactions, enhancing customer satisfaction. At the same time, advanced analytics enable banks to analyse behaviour patterns, predict trends, and personalise product offerings. Furthermore. enhancing cross-selling opportunities and driving deeper customer engagement. These tools are becoming strategic enablers for innovation in the financial landscape.
A Call to Action
For banks to fully realise the benefits of AI, they must address the digital transformation gap, modernising outdated infrastructures and fostering a culture of innovation. This includes investing in technologies that align with their strategic goals, ensuring robust data security measures alongside maintaining compliance with evolving regulations.
As the banking sector continues its journey towards digital maturity, AI will play a pivotal role in defining its future. By overcoming current barriers and embracing AI-driven solutions, banks can not only enhance operational efficiency but also deliver the seamless, personalised experiences that customers now expect in an increasingly digital world.
About Aryza
At Aryza know that in today’s highly regulated world, there is huge value in quickly guiding your customers through the product that best fit their immediate needs, through a seamless journey that is tailored to their specific circumstances.
We created smart platforms, responsible and compliant products, and a unique system of companies and capabilities so that businesses can optimise their customers’ journey through the right product at the right time.
For our teams across the globe, the growth of Aryza is a good news story and a testament to our clear vision and goals as an international business.
And also front of mind as we build a global footprint is our impact on the environment. Aryza is committed to reducing its carbon impact through the choices it makes and we are pleased to say that we follow an active roadmap.
Join FinTech’s greatest event when Money20/20 Europe returns to Amsterdam’s RAI Arena June 3-5 2025
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FinTech Strategy is proud to be a media partner for Money20/20 Europe 2025.
Launched by industry insiders in 2011, Money20/20 is the heartbeat of the global fintech ecosystem. Some of the most innovative, fast-moving ideas and companies have found their feet (and funding) on its show floor. From J.P. Morgan, Stripe, and Airwallex to HSBC, Deutsche Bank, and Checkout.com.
Furthermore, this is where you’ll find new connections, business-critical insights from inspirational speakers, innovation, and partnerships you need to ensure your business succeeds for whatever comes next in money.
Why Money20/20?
FinTech Strategy spoke with a host of leaders from across the FinTech spectrum. They all agreed on one thing, Money20/20 Europe is ‘the’ place to make connections and build your business.
“It’s the first time I’ve attended Money 20/20 and, we’ve had some fascinating impromptu conversations that will lead to great opportunities. All the big names are here and it’s clearly a popular event from a thematic perspective – payments is a big theme this year. I have a very high regard for the quality of what’s on offer and the way the event has been organised – it’s a great customer experience, the way it’s all been structured, at scale, is actually one of the best I’ve ever seen. The response has been fantastic…”
Stephen Everett, MD Payables & Receivables, Lloyds Banking Group
“The majority of people at Money20/20 genuinely get up in the morning with a growth and innovation mindset. Therefore, you have to balance and recognise that when you walk into this big venue that there will be some wacky ideas. From my experience, I have seen many infant ideas turn into successful ventures, whereas I have also seen some ventures becoming unsuccessful despite having great innovation ideas. Fintechs will fail. Innovation will fail. Experiments will fail. And that’s fine. That’s what Money20/20 is all about.”
Michelle Prance, CEO, Mettle (NatWest Group)
“It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank (NatWest) for fintechs. Quite often their route to market, route to capitalisation, is by going into a main bank being acquired. So, it’s that marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route to go down. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”
“The great thing about Money20/20, here in Europe, and in Asia and the US, is the good division between buyers and sellers. So, you have all these service providers like AirWallex, Amex, Stripe… And then you have the Heads of Payments from companies like Booking.com, Minted and Summit who are coming here with their team to meet with providers. If you think about that from a sales perspective, those meetings are very hard to get outside of this environment. But over a week you get 15 different meetings each day with that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations.”
“Paymentology is homegrown out of the UK so it’s important for us to make sure we’re representing the business across Europe. This is the centre of the world for banking innovation. We have customers here from Singapore, Dubai, Saudi Arabia, Ghana and beyond. People look to this event to really learn about what’s happening in the industry globally and discover what trends are going to come up. What should we be doing? How can we innovate together and learn from each other? That’s one of the things I really love about Money20/20; the talks in all of the panels are so interesting and I always leave knowing more. Being in the payments industry, and especially being an issue processor, it’s important for us to learn from the industry and understand where we need to move so that we can stay at the forefront of developments.”
“This is my sixth straight Money20/20 and it gets busier every year! It’s great to learn more about the ecosystem at large. You can see developing trends each year, and it’s always a little bit different. You build relationships at Money20/20 that stay with you for the rest of your life. And it’s a perfect opportunity to meet people in the flesh that you might normally only see on screen. You can get a pretty direct read on what they’re working on and it’s exciting to be here making new connections.”
FinTech Connect shapes the future of financial services with the UK’s only full FinTech ecosystem event at London’s Excel December 4-5
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Join us as FinTech Connect welcomes world leading Fintechs, Financial Institutions, Challenger Banks, Merchants, Scale-Ups and StartUps, Investors, Accelerators and Media to The ExceL, London.
FinTech Connect
Each year we welcome visionaries from the UK, Europe and beyond all looking to innovate within the market, expand their footprint and drive businesses forward. The event brings all this under one roof, over two insight-packed days, sparking ideas, forging partnerships and accelerating change.
Tackling the hottest topics and biggest challenges in the fintech market. Including: embedded finance, Web3, cross-border payments, investment, scaling, Gen AI, crypto, regulation, digital innovation and customer experience (CX).
Our mission is to connect the global thought leaders across the FinTech ecosystem in an event like no other. Set yourself up for a strong 2025 by signing up for the UK’s only full FinTech ecosystem event and join 2,000+ fintech leaders in London.
Insights from FinTech’s biggest names
We’ll be asking the big questions… What AI elements do financial institutions need to follow? Build, buy or partner? What opportunity works best in the modern ecosystem? How are banks advancing their digital transformations in 2024? Who owns the CX?
Gain insights on these topics and more from some of the biggest names in financial services. Speakers include Victoria Cleland, Executive Director – Payments, Bank of England; Rory Tanner, Head of UK Government Affairs at Revolut and Nick Kerrigan, Managing Director, Swift. Thought leaders will also be taking to the stage from HSBC, DZ Bank, Lloyds Banking Group, BT and a host of other leading institutions.
Keep up to date with the latest speakers, discussions and more. Download the full agenda here.
Scott Zoldi, Chief Analytics Officer at FICO considers whether the current AI bubble is set to burst, the potential repercussions of such an event, and how businesses can prepare
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Since artificial intelligence emerged more than fifty years ago, it has experienced cycles of peaks and troughs. Periods of hype, quickly followed by unmet expectations that lead to bleak periods of AI-winter as users and investment pull back. We are currently in the biggest period of hype yet. Does that mean we are setting ourselves up for the biggest, most catastrophic fall to date?
AI drawback
There is a significant chance of such a drawback occurring in the near future. So, the growing number of businesses relying on AI must take steps to prepare and mitigate the impact a drawback or complete collapse could have. Research from Lloyds recently found adoption has doubled in the last year, with 63% of firms now investing in AI, compared to 32% in 2023. In addition, the same study found 81% of financial institutions now view it as a business opportunity, up from 56% in 2023.
This hype has led organisations to explore AI use for the first time. Often with little understanding of the algorithms’ core limitations. According to Gartner, in 2023 less than 10% of organisations were capable of operationalising AI to enable meaningful execution. This could be leading to the ‘unmet expectations’ stage of the damaging hype/drawback cycle. The all-encompassing FOMO of repeating the narrative of the incredible value of AI does not align with organisations’ ability to scale, manage huge risks, or derive real sustained business value.
Regulatory pressures for AI
There has been a lack of trust in AI by consumers and businsses alike. It has resulted in new AI regulations specifying strong responsibility and transparency requirements for applications. The vast majority of organisations are unable to meet these in traditional AI, let alone newer GenAI applications. Large language models (LLMs) were prematurely released to the public. The resulting succession of fails fuelled substantial pressure on companies to pull back from using such solutions other than for internal applications. It has been reported that 60% of banking businesses are actively limiting AI usage. This shows that the drawback has already begun. Organisations that have gone all-in on GenAI – especially those early adopters – will be the ones to pull back the most, and the fastest.
In financial services, where AI use has matured over decades, analytic technologies exist today that can withstand regulatory scrutiny. Forward-looking companies are ensuring they are prepared. They are moving to interpretable AI and backup traditional analytics on hand while they explore newer technologies with appropriate caution. This is in line with proper business accountability, vs the ‘build fast, break it’, mentality of the hype spinners.
Customer trust with AI
Customer trust has been violated by repeated failures in AI, and a lack of businesses taking customer safety seriously. A pull-back will assuage inherent mistrust in companies’ use of artificial intelligence in customer facing applications and repeated harmful outcomes.
Businesses who want their AI usage to survive the impending winter need to establish corporate standards for building safe, transparent, trustworthy Responsible AI models that focus on the tenets of robust, interpretable, ethical and auditable AI. Concurrently, these practices will demonstrate that regulations are being adhered to – and that their customers can trust AI. Organisations will move from the constant broadcast of a dizzying array of possible applications, to a few well-structured, accountable and meaningful applications that provide value to consumers, built responsibly. Regulation will be the catalyst.
Preparing for the worst
Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of algorithm mathematics and the very signifiicant risk in using the technology.
Stringing together AI is easy. Building AI that is responsible and safe is a much harder and exhausting exercise requiring model development and deployment corporate standards. Businesses need to start now to define standards for adopting the right types of AI for appropriate business applications, meet regulatory compliances, and achieve optimal consumer outcomes.
Companies need to show true data science leadership by developing a Responsible AI programme or boosting practices that have atrophied during the GenAI hype cycle which for many threw standards to the wind. They should start with a review of how regulation is developing, and whether they have the standards, data science staff and algorithm experience to appropriately address and pressure-test their applications and to establish trust in AI usage. If they’re not prepared, they need to understand the business impacts of potentially having artificial intelligence pulled from their repository of tools.
Next, these companies must determine where to use traditional AI and where they use GenAI, and ensure this is not driven by marketing narrative but meeting both regulation and real business objectives safely. Finally, companies will want to adopt a humble approach to back up their deployments, to tier down to safer tech when the model indicates its decisioning is not trustworthy.
Now is the time to go beyond aspirational and boastful claims, to have honest discussions around the risks of this technology, and to define what mature and immature AI look like. This will help prevent a major drawback.
Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors
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There is no single solution, AI or otherwise, that can prepare financial institutions for the modern world. To build a bank capable of successfully navigating the challenges of the future, a long-term digital transformation strategy is required. Especially relevant in the wake of recent IT outages,
At present, according to Endava’s Retail Banking Report 2024, 67% of banks are still heavily reliant on legacy systems. This leads to wasted budget and decreased efficiency. With limited resources available to modernise their tech stack, company leaders are often forced to choose which technology-type to prioritise. When doing this, 50% have chosen artificial intelligence (AI).
Is AI alone enough?
Can AI overhaul archaic processes or are there too many hurdles in the way? The first hurdle to successful digital transformation in financial services is overcoming the employees’ perception of the process. Time and time again, corporations have failed in the goal to integrate solutions that successfully feed into a long-term tech strategy. Often, this is due to wide-spread change fatigue. When exhausted by continuous efforts to change their day-to-day, workers become resistant to transformation. The best way to overcome change fatigue, and drive digital transformation in financial institutions, is through overhauling legacy systems. And adopting solutions that will stand the test of time.
Legacy Systems
Across the world, outdated legacy systems are holding financial institutions back and costing them billions. From 2022 to 2028, this expense is expected to grow at a rate of 7.8%. Not only do these archaic processes cost money, but they force banks to contend with a multitude of siloes. From departments to data. We live in a world where neobanks are growing in popularity. They are able to provide a frictionless customer experience using their modern tech stack. Traditional organisations must rid themselves of siloes to enable all areas of the business to leverage AI. In turn, this will provide them with strong data collection and support from departments who are agreed on next steps.
At present, three quarters of financial institutions feel they need to modernise their core. Without this change, they lack the secure, data-driven foundation necessary to utilise AI and see return on their technical investments.
The benefits of AI integration
Once a strong foundation has been laid, it becomes easier to see the practical benefits of integrating AI. For example, when data is no longer siloed by legacy systems, using chat bots to support customers with simple queries creates an efficient consumer experience. There are internal benefits too. AI can spot potentially suspicious activity, flagging it before it is too late. Or analysing data to ensure risk management and process automation. Despite its wide-reaching capabilities, AI alone is not the only option for financial institutions…
Routes to the future
Endava’s Retail Banking Report also showcased the variety of solutions that banks are using to improve their tech stack. 45% of respondents recognised data analytics, in and of themselves, as a top area for investment. Meanwhile 30% flagged IoT, and 14% the Metaverse.
There’s a reason for the emphasis on strong data. It not only supports the integration and use of AI-fuelled capabilities, but it is the driving force behind numerous functions of the bank itself. Of those surveyed, 37% aimed to use data to improve customer service. 34% to strengthen security, and 33% to personalise products and improve the customer experience.
As well as attracting and retaining consumers, business leaders can benefit from their access to strong data by attracting and retaining talent. With 39% of failed digital transformations viewing lack of employee buy-in as a factor, financial institutions are encouraged to educate workers on their technology integration plans, and ensure solutions are user-friendly. Fortunately, looking ahead, 20% of banks surveyed seek to use data to improve the workplace.
A bank’s priority – looking ahead
More than ever, banks are reliant on data to keep operations running smoothly. From providing customers with a personalised experience to improving the workplace in the competition for talent, there are a multitude of reasons to ensure the foundations of your tech stack are strong.
Doing so makes integration of new technology a smoother experience for all. To this end, it’s no shock that 50% of banks are keen to embrace AI, using it to benefit customers and speed up processes. However, with many hampered by the legacy technology and the ever-looming threat of change fatigue, integration of any technology should be carefully planned, customer focused and data led.
Gabe Hopkins, Chief Product Officer at Ripjar, on how GenAI can transform compliance
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Generative AI (GenAI) has proven to be a transformational technology for many global industries. Particularly those sectors looking to boost their operational efficiency and drive innovation. Furthermore, GenAI has a range of use cases, and many organisations are using it to create new, creative content on demand – such as imagery, music, text, and video. Others are using the new tools at their disposal to perform tasks and process data. This makes previously tedious activities much more manageable, saving considerable time, effort, and finances in the process.
However, compliance as a sector has traditionally shown hesitancy when it comes to implementing new technologies. Taking longer to implement new tools due to natural caution about perceived risks. As a result, many compliance teams will not be using any AI, let alone GenAI. This hesitancy means these teams are missing out on significant benefits. Especially at a time when other less risk-averse industries are experiencing the upside of implementing this technology across their systems.
To avoid falling behind other diverse industries and competitors, it’s time for compliance teams to seriously consider AI. They need to understand the ways the technology – specifically GenAI – can be utilised in safe and tested ways. And without introducing any unnecessary risk. Doing so will revolutionise their internal processes, save work hours and keep budgets down accordingly.
Understanding and overcoming GenAI barriers
GenAI is a new and rapidly developing technology. Therefore, it’s natural compliance teams may have reservations surrounding how it can be applied safely. Particularly, teams tend to worry about sharing data, which may then be used in its training and become embedded into future models. Moreover, it’s also unlikely most organisations would want to share data across the internet. Strict privacy and security measures would first need to be established.
When thinking about the options for running models securely or locally, teams are likely also worried about the costs of GenAI. Much of the public discussion of the topic has focussed on the immense budget required for preparing the foundation models.
Additionally, model governance teams within organisations will worry about the black box nature of AI models. This puts a focus on the possibility for models to embed biases towards specific groups, which can be difficult to identify.
However, the good news is that there are ways to use GenAI to overcome these concerns. This can be done by choosing the right models which provide the necessary security and privacy. Fine-tuning the models within a strong statistical framework can reduce biases.
In doing so, organisations must find the right resources. Data scientists, or qualified vendors, can support them in that work, which may also be challenging.
Overcoming the challenges of compliance with AI
Despite initial hesitancy, analysts and other compliance professionals are positioned to gain massively by implementing GenAI. For example, teams in regulated industries – like banks, fintechs and large organisations – are often met with massive workloads and resource limits. Depending on which industry, teams may be held responsible for identifying a range of risks. These include sanctioned individuals and entities, adapting to new regulatory obligations and managing huge amounts of data – or all three.
The process of reviewing huge quantities of potential matches can be incredibly repetitive and prone to error. If teams make mistakes and miss risks, the potential impact for firms can be significant. Both in terms of financial and reputational consequences.
In addition, false positives – where systems or teams incorrectly flag risks and false negatives – where we miss risks that should be flagged, may come from human error and inaccurate systems. They are hugely exacerbated by challenges such as name matching, risk identification, and quantification.
As a result, organisations within the industry quite often struggle to hire and retain staff. Moreover, this leads to a serious skills shortage amongst compliance professionals. Therefore, despite initial hesitancy, analysts and other compliance professionals stand to gain massively by implementing GenAI without needing to sacrifice accuracy.
Generative AI – welcome support for compliance teams
There are numerous useful ways to implemented GenAI and improve compliance processes. The most obvious is in Suspicious Activity Report (SAR) narrative commentary. Compliance analysts must write a summary of why a specific transaction or set of transactions is deemed suitable in a SAR. Long before the arrival of ChatGPT, forward thinking compliance teams were using technology based on its ancestor technology to semi-automate the writing of narratives. It is a task that newer models excel at, particularly with human oversight.
Producing summarised data can also be useful when tackling tasks such as Politically Exposed Persons (PEP) or Adverse Media screenings. This involves compliance teams performing reviews or research on a client to check for potential negative news and data sources. These screenings allow companies to spot potential risks. It can prevent them from becoming implicated in any negative relationships or reputational damage.
By correctly deploying summary technology, analysts can review match information far more effectively and efficiently. However, like with any technological operation, it is essential to consider which tool is right for which activity. AI is no different. Combining GenAI with other machine learning (ML) and AI techniques can provide a real step change. This means blending both generalised and deductive capabilities from GenAI with highly measurable and comprehensive results available in well-known ML models.
Profiling efficiency with AI
For example, traditional AI can be used to create profiles, differentiating large quantities of organisations and individuals separating out distinct identities. The new approach moves past the historical hit and miss where analysts execute manual searches limiting results by arbitrary numeric limits.
Once these profiles are available, GenAI can help analysts to be even more efficient. The results from the latest innovations already show GenAI-powered virtual analysts can achieve, or even surpass, human accuracy across a range of measures.
Concerns about accuracy will still likely impact the rate of GenAI adoption. However, it is clear that future compliance teams will significantly benefit from these breakthroughs. This will enable significant improvements in speed, effectiveness and the ability to respond to new risks or constraints.
Ripjar is a global company of talented technologists, data scientists and analysts designing products that will change the way criminal activities are detected and prevented. Our founders are experienced technologists & leaders from the heart of the UK security and intelligence community all previously working at the British Government Communications Headquarters (GCHQ). We understand how to build products that scale, work seamlessly with the user and enhance analysis through machine learning and artificial intelligence. We believe that through this augmented analysis we can protect global companies and governments from the ever-present threat of money laundering, fraud, cyber-crime and terrorism.
Sejal Mehta and Andrew Rodgers from Odgers Berndtson’s Global FinTech Centre of Excellence and Randy Bean, a Senior Advisor to Odgers Berndtson and industry author, explore the dynamics shaping leadership in the UK fintech sector
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The UK FinTech sector is undergoing a significant transformation, marked by maturation, consolidation, and a more selective investment landscape. Funding is increasingly funnelled towards profit-generating scale-ups, and away from newer entrants.
At the same time, the sector is shaped by a multi-generational workforce with varied perspectives. Meanwhile rapid advancements in AI foster apprehension and excitement. These converging factors make FinTech one of the most dynamic and competitive spaces to work in today. This presents both challenges and opportunities for its leaders.
From our perspective as global FinTech executive search and leadership advisors at Odgers Berndtson these shifts are reshaping the demands placed on leadership. They are also influencing what it takes to lead effectively in this fast-changing sector. Here, we explore the leadership trends that are emerging as a result.
Ethical FinTech leadership
Venture capital funding is now more selective and private equity investors are increasingly targeting fintechs with solid exposure. This is creating a difficult environment for new start-ups. Those attracting funding are typically cash-positive scale-ups.
Amidst these challenges, more FinTech firms are opting to list on the NASDAQ rather than the London Stock Exchange, as the UK navigates more stringent regulation. The need for payments licences, extensive reporting, and compliance demands weigh heavily on FinTech leaders.
In this landscape, we’re seeing leaders with experience in regulated financial services bring a valuable skillset. The ability to operate within defined regulatory frameworks while generating growth. FinTech boards are looking for leaders with high authenticity and who can make ethical decisions. And while balancing ambition and growth with the realities of working in a highly regulated space.
Founder replacements
We are in the midst of the FinTech sector’s maturation. Start-ups are transitioning into scale-ups, requiring different leadership competencies. For many, this requires the founder to step down or step into a board role and appoint a CEO who can take the business through its next stage of growth.
This requires leaders who are commercially driven, capable of shaping market strategies, and adept at understanding customer needs and product-market fit. Navigating risk and regulation becomes crucial, while the founder’s creative, opportunity-led approach typically no longer dominates the new operational and strategic demands.
Boards and investors are looking for CEOs with a broader skillset and deep regulatory expertise. These leaders must also be able to attract and retain the type of talent that can sustain growth and innovation, while maintaining the ‘DNA’ that made the business so attractive in the first place.
A multi-generational workforce
Intergenerational divides are becoming more pronounced for all businesses and noticeably in sectors like FinTech. Here, younger generations with fresh perspectives are working alongside older, more experienced professionals – often from traditional financial services backgrounds.
This diversity in age, experience, and approach can be a powerful asset, but only if integrated effectively. Typically, Gen Z and Millennials prioritise flexibility, technological integration and experimentation. Meanwhile, Boomers bring valuable expertise in regulatory environments and operational effectiveness, but may be more accustomed to traditional structures and leadership styles.
Increasingly, we see FinTech leaders attempt to bridge these divides by emphasising open communication, promoting mentorship opportunities, and encouraging cross-generational collaboration. With less funding and more regulation, FinTech leaders recognise the need to identify and capitalise on the strengths of a multigenerational workforce if they are to succeed.
Leadership team dynamics
As FinTech companies scale, leadership is no longer just about the capabilities of individual leaders but about the dynamics of the entire executive team. Successful scale-ups understand the importance of assembling a leadership team that brings a diverse mix of skills, and generational perspectives to the table.
We are starting to see FinTech companies think about leadership team dynamics as they scale up. Boards are looking for a blend of strategic, operational and ethical considerations. As well as how well team members work together. Do they solve problems cohesively? Are there any unresolved tensions or conflict? Are they aligned and equipped to collectively deliver on the leadership mandate?
Many leadership teams are not optimising their potential due to misalignment of strengths. For example, we recently worked with a FinTech creating an executive team profile to identify the leadership competencies needed to deliver their mandate. This exercise enabled the team to reallocate executive responsibilities for strategic initiatives based on the required strengths, regardless of traditional job roles.
Polarising views on Gen AI
Leading organisations are experiencing a transformational moment due to accelerated interest in AI and Generative AI. 89.6% are increasing their investments in AI, while 64.2% of companies have indicated that AI will be the most transformational technology in a generation. In response, organisations are hiring for the data and AI leadership roles required to prepare their companies for an AI future.
However, this integration of Gen AI has sparked both excitement and nervousness, particularly around issues of data protection and privacy. Generational differences are especially noticeable. Younger professionals are often less concerned about data privacy, while older generations remain cautious about the security implications.
This divergence in attitudes can create tension within the organisation, as leaders grapple with how best to leverage Gen AI while ensuring compliance with stringent data protection regulations. For some FinTechs, AI is seen as a specialised area requiring dedicated focus. Meanwhile, others believe AI represents a fundamental shift in how business can be conducted and AI strategy should be woven into the fabric of every leader’s responsibilities.
This divide in attitudes reflects the broader challenges we see FinTech companies face in incorporating AI. Leaders must now navigate the balance between embracing innovation and safeguarding sensitive information. They must also ensure AI is not seen as a siloed function. It must be an integral part of their commercial and strategic vision. Given the fundamental changes in the sector, the emphasis on leadership capabilities is changing for both the individual and executive team.
Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services
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We’ve seen an enormous amount of discussion concerning all aspects of AI since the emergence of Chat GPT made it headline news. However, most articles and conversations focusing on its business impact seem to wilfully ignore the ‘elephant in the room’. Namely, the inevitable organisational change AI will usher in, especially for employees.
AI technology driving change
To ignore change is folly, and likely to have the exact opposite effect that businesses and AI technology vendors want. We can’t pretend workforces won’t be disrupted by such a seismic technological advance. Certain job roles will become obsolete. Business leaders can’t run the risk of creating a culture of fear and uncertainty among employees who are unlikely to be fooled.
It’s true AI could lead to leaner operations, particularly in insurance and finance companies, with fewer employees needed for routine tasks, but only half the story. Smart businesses will almost certainly reinvest cost savings into new growth areas that require specific human talent. Companies that maintain a strong human element in customer service and personalised offerings will differentiate themselves in a crowded market. The rise in AI-driven, agile companies will create faster market shifts and greater competition.
While AI has the potential for productivity and efficiency gains, and even to do the same with less if needed, I actually don’t predict major job culls in the next few years. AI is particularly good at data processing and data analytics, in insurance for example. So, when more data can be processed and analysed, human intervention can make better informed decisions as a result. In the short to medium term, data analysis and decision making will remain firmly in the human realm. But powered by AI.
The Future for Artificial Intelligence
Meanwhile, the technology is still evolving, and organisations need to build a model that layers over the top of AI – powered by it, rather than replaced by it. Despite the hype, we are still a long way from AI becoming an entity that can lead, implement and operate itself to a purposeful end. But it will increasingly power applications overlaid by strategic, human-led frameworks.
To achieve this, leaders must bring their teams with them on the journey. In the field of testing for example, developers have traditionally written code as part of their role. This is a very time consuming and laborious task. Historically skills gaps have led to delays in progress. But the ability to ‘outsource’ to AI has freed up the time of those developers to focus on the purpose of that code in relation to the product. And, ultimately, the customer. Similarly, leaders in all fields need a broader understanding of AI use cases such as these to make effective strategic decisions. For example, on hiring. Understanding when to bring in more people and when to bring in new technology to complement the skills of your existing team means understanding AI’s strategic implications, technical capabilities and limitations.
An Evolving Job Market
From the perspective of the employee, the job market will continuously evolve alongside AI advancements. It will require ongoing adaptation and learning to stay relevant. Skills such as empathy, communication, and negotiation will remain vital. These are differentiators and difficult for AI to replicate. Understanding AI tools and data analysis will be increasingly important, even for non-technical roles. The ability to adapt to new technologies and continuously learn will be essential. Moreover, as AI becomes more integrated, the need for professionals who understand the ethical implications and regulatory requirements will grow exponentially.
Driving growth and job creation in this new world will require a different mindset to the current received wisdom. From both employees and leaders. In addition to the advances and changes already discussed, AI also has the potential to level the playing field, enabling smaller or newer companies to compete more effectively with, and even seriously threaten, established players. With many traditional barriers to entry such as burdensome start-up costs removed, new business models are likely to emerge. In much the same way as they did in the early days of the internet. Investors will be on the lookout for the next ‘giant killer’.
This will create opportunities for those with the foresight to upskill, as well as for those looking to start their careers. Although those opportunities and the jobs of tomorrow may not yet be completely clear. What is clear, however, is that established businesses cannot afford to be complacent. Change is inevitable and empires can be toppled overnight by technology as disruptive as AI. By embracing it early, leaders in those businesses will have the opportunity to spot and fix the gaps and redundancies in their business models that the technology and its capabilities exposes before the market does so more painfully and publicly.
Our mission is to enable and lead the world’s quality-first revolution. QA tools haven’t kept up with the demands of the testing world. Virtuoso is here to deliver with AI-powered, low-code/no-code test automation to support the modern business.
“Virtuoso technology represents the foundation for software quality in the digital world, and we are proud to be a critical, guiding force in the era of AI.”
Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services
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There’s a lot of buzz around Generative AI (GenAI). What’s not always heard beneath the noise are the very real and serious risks of this fast-developing AI tech. Let alone ways to mitigate these emerging threats.
Currently, one quarter (26%) of accounting and bookkeeping practices in the UK have now adopted GenAI in some capacity. That figure is predicted to grow for many years to come.
With this in mind, and as we hit the crest of the GenAI hype cycle, it’s critically important that leaders focus closely on the potential risks of AI deployment. They need to proactively prepare to mitigate them, rather than picking up the pieces after an incident.
Navigating the risky transition to AI
The benefits of AI are well-proven. For finance teams, AI is a powerup that unlocks major performance and efficiency boosts. It significantly enhances their ability to generate actionable insights swiftly and accurately, facilitating faster decision-making. AI isn’t here to take over but to augment the employees’ capabilities. Ultimately improving leaders’ trust in the reliability of financial reporting.
One of the most exciting aspects of AI is its potential to enable organisations to do more with less. Which, in the context of an ongoing talent shortage in accounting, is what all finance leaders are seeking to do right now. By automating routine tasks, AI empowers accountants to focus on higher-level analysis and strategic initiative, whilst drawing on fewer resources. GenAI models can help to perform routine, but important tasks. These include producing reports for key stakeholders and ensuring critical information is effectively and quickly communicated. It enables timely and precise access to business information, helping leaders to make better decisions.
However, GenAI also represents a new source of risk that is not always well understood. We know that threat actors are using GenAI to produce exploits and malware. Simultaneously levelling up their capabilities and lowering the barrier of entry for lower-skilled hackers. The GenAI models that power chatbots are vulnerable to a growing range of threats. These include prompt injection attacks, which trick AI into handing over sensitive data or generating malicious outputs.
Unfortunately, it’s not just the bad guys who can do damage to (and with) AI models. With great productivity comes great responsibility. Even an ambitious, forward-thinking, and well-meaning finance team could innocently deploy the technology. They could inadvertently make mistakes that cause major damage to their organisation. Poorly managed AI tools can expose sensitive company and customer financial data, increasing the risk of data breaches.
De-risking AI implementation
There is no technical solution you can buy to eliminate doubt and achieve 100% trust in sources of data with one press of a button. Neither is there a prompt you can enter into a large language model (LLM).
The integrity, accuracy, and availability of financial data are of paramount importance during the close and other core accountancy processes. Hallucinations (another word for “mistakes”) cannot be tolerated. Tech can solve some of the challenges around data needed to eliminate hallucinations – but we’ll always need humans in the loop.
True human oversight is required to make sure AI systems are making the right decisions. We must balance effectiveness with an ethical approach. As a result, the judgment of skilled employees is irreplaceable and is likely to remain so for the foreseeable future. Unless there is a sudden, unpredicted quantum leap in the power of AI models. It’s crucial that AI complements our work, enhancing rather than compromising the trust in financial reporting.
A new era of collaboration
As finance teams enhance their operations with AI, they will need to reach across their organisations to forge new connections and collaborate closely with security teams. Traditionally viewed as number-crunchers, accountants are now poised to drive strategic value by integrating advanced technologies securely. The accelerating adoption of GenAI is an opportunity to forge links between departments which may not always have worked closely together in the past.
By fostering a collaborative environment between finance and security teams, businesses can develop robust AI solutions. They can boost efficiency and deliver strategic benefits while safeguarding against potential threats. This partnership is essential for creating a secure foundation for growth.
AI in accountancy: The road forward
The accounting profession stands on the threshold of an era of AI-driven growth. Professionals who embrace and understand this technology will find themselves indispensable.
However, as we incorporate AI into our workflows, it is crucial to ensure GenAI is implemented safely and does not introduce security risks. By establishing robust safeguards and adhering to best practices in AI deployment, we can protect sensitive financial information and uphold the integrity of our profession. Embracing AI responsibly ensures we harness its full potential while guarding against vulnerabilities, leading our organisations confidently into the future.
Founded in 2013, FloQast is the leading cloud-based accounting transformation platform created by accountants, for accountants. FloQast brings AI and automation innovation into everyday accounting workflows, empowering accountants to work better together and perform their tasks with greater efficiency and accuracy. Now controllers and accountants can spend more time delivering greater strategic value while enjoying a better work-life balance.
Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech
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Strict regulation, along with time and cost restraints, means financial services must take a measured approach to technological advancements. However, with the emergence of GenAI, particularly large language models (LLMs), organisations have an opportunity to maximise the value of their data to streamline internal operations and enhance efficiencies.
Embracing GenAI has never been more important for organisations looking to stay ahead of the curve. 40-60% of the global workforce will be impacted by the growth of AI. Moreover, global adoption of GenAI could add the equivalent of $2.6tn to $4.4tn in value annually to global industries. The banking sector stands to gain between $200-340 billion.
But whilst the financial services industry can gain incredible benefits from GenAI, adoption is not without its challenges. Financial organisations must prioritise responsible data management. They must also navigate strict privacy regulations and carefully curate the information they use to train their models. But, for companies that persevere through these obstacles, the benefits will be substantial.
Building customised LLMs for financial services
Consumer chatbots have brought GenAI to the mainstream. Meanwhile, the true potential of this transformative technology lies in its ability to be tailored to the unique needs of any organisation, in any industry. Including the financial sector.
Risk assessment, fraud prevention, and delivering personalised customer experiences are some of the use cases of custom open source models. Created using a company’s proprietary data, these models ensure relevant and accurate results. And are more cost-effective due to their smaller datasets. For instance, banks can use a customised model to seamlessly analyse customer behaviour and flag up any suspicious or fraudulent activities. Or, a model can leverage sophisticated algorithms to assess an individual’s eligibility for a loan.
Another huge benefit of these tailored systems is trust and security. Deploying a custom open-source model eliminates the need to share sensitive information with third parties. This is crucial for organisations operating within such a highly regulated industry. This approach also democratises the training of custom models. Furthermore, it allows organisations to harness the power of GenAI whilst retaining control and compliance.
Using data intelligence to boost AI’s impact
To truly harness the power of GenAI, organisations must cultivate a deep understanding of data across the entire workforce. Every employee, regardless of how technical they are, must grasp the importance of proper data storage. Also how data can be used to improve decision-making.
Organisations can use a data intelligence platform to help implement this. Built on a lakehouse architecture, a data intelligence platform provides an open, unified foundation for all data and governance. It operates as a secure end-to-end solution tailored to the specific needs of the financial services industry. By adopting such a platform, businesses can eliminate their reliance on third party solutions for data analysis. They can create a streamlined approach to data governance and accelerate data-driven outcomes. Users across all levels of the business can navigate their organisation’s data, using GenAI to uncover important insights.
The future of AI in the financial sector
The path to success lies in embracing GenAI as a canvas for crafting bespoke solutions. Whilst no two financial institutions are exactly the same, the industry’s tools must strike a delicate balance between supporting specific use cases and addressing broader requirements, Customised, open source LLMs and data intelligence platforms hold the key, sparking transformative change across the sector. These tailored solutions will empower financial businesses to integrate cutting-edge innovations and ensure security, governance and customer satisfaction. Organisations that embrace this change will not only gain a competitive edge, but also pave the way for larger transformations, re-shaping the financial landscape and setting new standards for the industry.
Databricks is the data and AI company with origins in academia and the open source community. Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.
Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?
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Visit any social media newsfeed and countless posts will tell you AI means “nothing will ever be the same again”. Or even that “you’re doing AI wrong”. The volume of hyperbolic opinions being pushed makes it almost impossible for businesses to decipher between hype and reality.
This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence. It sets out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.
Within the debate about AI in financial services, B2B payments are an area where AI has huge potential to accelerate digital innovation. Let’s go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.
Understanding what AI is, and what it isn’t
AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML). ML has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.
A newer part of this is ‘deep learning’, which leverages multi-layered neural networks. This simulates the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLMs). LLMs are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data. It is becoming capable of making informed decisions based on this data.
Before we explore what this means for B2B payments, let’s make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing. You cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments. AI tools will help you learn, adapt and improve more quickly and at scale.
The AI Act – what you need to know
The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:
Unacceptable risk – AI systems deemed a threat to people, which will be banned. This includes systems involved in cognitive behavioural manipulation, social scoring, and real-time biometric identification.
High risk – AI systems that negatively affect safety or fundamental rights. High-risk AI systems will undergo rigorous assessment and must adhere to stringent regulatory standards before being put on the market. These high risk systems will be divided into two further categories:
AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
AI systems falling into specific areas that will have to be registered in an EU database.
The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk. However, it will have to comply with transparency requirements and EU copyright law.
High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations. Any serious incidents would have to be reported to the European Commission.
The Act aims to become fully applicable by May 2026. Following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November 2024, the EU will start banning ‘unacceptable risk’ AI systems. And by February 2025 the ‘codes of practice’ will be applied.
So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?
Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.
AI-based LLMs and ML can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.
One tangible benefit of this data analysis for businesses comes from combining payment data with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.
Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.
AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.
Efficient data flows mean stronger cash flows
Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.
AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.
By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.
The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.
Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.
This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency. Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.
When. Not if.
There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.
While all the opportunities for AI in B2B payments outlined here are based on relatively low-risk AI systems, human oversight of these systems is still essential. However, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.
AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than fearing it or passing full control over to it.
In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.
Most businesses will likely wait for the early adopters to fail, learn and progress. If something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the hype claims.
“Adflexhas been at the heart of the B2B fintech revolution from the beginning. We are known for fostering innovation and helping companies harness the power of digital payments. Our technology and expertise bring together buyers and suppliers to make transactions fast, cost-effective and straightforward to manage. We take the pain out of the supply chain by delivering seamless and secure payment integration that adds value to both buyers and merchants.”
Michael Donnelly, Head of Client Success at BlueFlame AI, on how to prepare your firm to attract and retain the next generation of AI talent
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In the fast-paced world of financial services, a new generation is stepping in with high expectations for generative artificial intelligence (AI) in the workplace. Recently, BlueFlame AI conducted a specialised training session for one of our private equity clients, aimed at their newly hired summer intern class. The experience was eye-opening for us. Furthermore, it also provided a great lesson in the growing importance of AI in the industry and the expectations today’s young professionals have as they enter the workforce
AI & LLMs
The comprehensive training session covered vital areas such as AI and Large Language Models (LLMs), a review of the most popular use cases the industry has adopted, and hands-on practical training in prompt engineering. Moreover, our goal was to show this next generation the skills they’ll need to leverage these tools effectively. New roles could revolutionise alternative investment management processes like due diligence, market analysis, and portfolio management.
We also used this as an opportunity to survey the group about their experience of and expectations for AI use in the workplace – and it yielded some striking insights. A significant 50% of the interns reported using ChatGPT daily, with 83% utilising it at least weekly. Furthermore, these numbers suggest young professionals expect these tools to be available to them in their professional lives. In the same way they are available in their personal lives and set to become as commonplace as traditional software in the workplace. The interns’ expectations regarding AI’s impact on their work efficiency are even more telling. An overwhelming 94% believe these tools will enhance their productivity, indicating strong faith in the technology’s potential to streamline tasks and boost performance.
These high expectations have key implications for employers. A significant 89% of interns expect their employers to provide enterprise-grade AI/LLM access. This statistic is a wake-up call for companies that have yet to invest in AI technologies, highlighting the need to stay competitive not just in terms of products and services but also in workplace technology provision.
Talent Acquisition & Retention
Perhaps most important is AI’s potential impact on talent acquisition and retention. One-third (33%) of interns surveyed indicated they would reconsider their choice of employer if they didn’t offer access to enterprise-grade AI/LLM tools. A response that could throw a serious wrench into any Financial Services firm’s hiring plans.
The message is clear for businesses looking to stay ahead of the curve when it comes to supporting their employees. Investing in AI technologies and training is no longer optional. Firms must be ready to meet the expectations of the incoming workforce. They need to provide them with the best technology to maintain a competitive edge in an increasingly AI-driven business landscape. Companies that embrace AI and provide their employees with the tools and training to harness its power will likely see significant productivity, innovation, and talent retention advantages.
AI Revolution
Private and public investment firms stand to benefit greatly from this AI revolution. As this new generation brings its enthusiasm and expectations for technology tools into the workplace, firms that are prepared to meet these expectations will be better positioned to tap into fresh perspectives, drive innovation and reap significant efficiency and productivity gains. And if firms can take a proactive approach to training and commit to developing a forward-thinking, AI-enabled workforce, they will be able to enhance their teams’ capabilities and shape the future of work in the financial sector.
Generative AI and the workplace expectations it has created mark a new paradigm in the market. The next generation of professionals is not just ready for AI – they’re demanding it. Firms that recognize and act on this trend will be well-positioned to lead the pack when it comes to innovation, efficiency and talent acquisition.
Founded in 2023 BlueFlame AI is the only AI-native, purpose built, LLM-agnostic solution for Alternative Investment Managers.
Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…
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Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer support, and automate processes, making banks more efficient and customer-focused.
Research by McKinsey shows that over 20 percent of an organisation’s digital budget goes towards AI. The study links significant investments in AI to a 10-20 percent increase in sales. AI will play a central role in boosting efficiency, customer service, and overall banking productivity.
Introduction to AI in Personalised Banking
Delivering personalised experiences is crucial for customer satisfaction and retention. AI helps banks achieve this by collecting and analysing customer data. This data is then used to create recommendations, product offerings, and even financial advice tailored to each customer’s needs.
AI tools can optimise workflows through a technique called prescriptive personalisation, using past data to predict future behaviour. Real-time personalisation takes this further, incorporating current information alongside historical data.
This allows banks to deliver highly customised virtual assistants and real-time recommendations powered by natural language processing (NLP) models. These AI-powered assistants not only build trust and user engagement but also simplify interactions with the bank.
Tool 1: Predictive Analytics
Predictive analytics, powered by AI tools, unlock a new level of customer personalisation in banking. These tools analyse data to uncover hidden patterns and trends that traditional methods might miss. This knowledge reveals sales opportunities, possibilities for cross-selling, and ways to improve efficiency.
Predictive analytics use past data to forecast customer behaviour and market trends. This foresight allows banks to tailor marketing strategies and sales approaches to meet changing customer needs and capitalise on emerging opportunities.
Tool 2: Chatbots and Virtual Assistants
One key advantage of chatbots is their constant availability. This is especially helpful for customers who need assistance outside of regular operating hours.
AI chatbots learn from every interaction, improving their ability to understand and meet individual customer needs. By integrating chatbots into banking apps, banks can provide personalised banking experiences and recommend financial products and services that fit a customer’s specific situation.
Erica, a virtual assistant developed by Bank of America, handles tasks like managing credit card debt and updating security information. With over 50 million requests handled in 2019 alone, Erica demonstrates the potential of chatbots as efficient assistants for customers.
Tool 3: Recommendation Engines
Banks use AI tools to analyse vast amounts of customer data, including purchases, browsing habits, and background information. This deep understanding helps banks recommend products that truly fit each customer’s needs.
These personalised recommendations extend beyond credit card suggestions. AI can identify potential investments or loans that align with a customer’s financial goals. By providing customers with relevant information, banks allow them to make informed financial decisions.
Tool 4: Sentiment Analysis with AI
AI sentiment analysis translates written text into valuable insights. AI uses NLP to understand emotions and opinions in written communication. By examining things like customer feedback, emails, and social media conversations, banks gain a much clearer picture of customer sentiment.
Tool 5: Voice Recognition
AI-powered voice assistants offer a convenient way to handle everyday banking tasks. From checking balances to paying bills, all a customer needs are simple voice commands.
These assistants use NLP to understand customer requests and respond accurately. Voice authentication adds another layer of security by verifying customer identity during transactions.
Tool 6: Process Automation
Robotic Process Automation (RPA) automates repetitive tasks, boosting operational efficiency. It tackles up to 80 percent of routine work and frees up workers for more valuable tasks requiring human judgement.
RPA bots can handle tasks like issuing and scheduling invoices, reviewing payments, securing billing, and streamlining collections – all at once. NLP empowers these bots to extract information from documents, simplifying application processing and decision-making.
Tool 7: Facial Recognition with AI
Facial recognition helps banks verify customer identities during tasks like opening accounts, accessing information, and making transactions. Compared to traditional passwords, facial recognition offers stronger security and greater convenience. It eliminates the need for remembering complex passwords or worrying about stolen credentials, making banking interactions smoother and less error-prone. This technology also helps prevent fraud by identifying attempts to impersonate real customers.
Capital One AI Case Study
Capital One demonstrates how AI can personalise banking. Their AI assistant uses NLP to understand customer questions and provide immediate answers. Capital One also incorporates AI into fraud detection. Machine learning and predictive analytics help pinpoint suspicious credit card activity to strengthen security measures.
Conclusion
AI tools offer a significant opportunity for banks to improve customer experiences and achieve long-term success. By personalising banking services with AI, banks can better meet individual customer needs. This leads to higher satisfaction and loyalty, which enhances the bank/customer relationship.
AI has the potential for an even greater impact. As banks integrate more advanced AI capabilities, they can create even more engaging and personalised interactions. This focus on ‘hyper-personalisation’ could be the next big step for financial institutions to set them apart in a competitive market.
Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…
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Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects that 75 percent of financial institutions will invest $31 billion in integrating AI into their existing systems by 2025. The trend is driven by customer demand for faster and more convenient banking options.
AI excels at analysing enormous amounts of data. This lets banks find patterns and trends to personalise customer service and boost efficiency. For example, AI-powered chatbots offer 24/7 help with basic questions, freeing up customer service staff for trickier issues. AI can also analyse customer behaviour to predict their needs and suggest relevant services or support, from personalised investment options to flagging suspicious account activity.
Benefit 1: Increased Efficiency
Long wait times and impersonal interactions often leave customers frustrated with traditional bank customer service. Fortunately, AI streamlines the experience by providing quick and accurate answers. It eliminates the need to navigate complex phone menus.
AI personalises interactions and saves customers from endless button-pressing and long hold times. AI in customer service can also analyse vast amounts of customer data. The data helps banks anticipate customer needs and recommend tailored solutions, preventing problems before they arise. This results in higher customer satisfaction and a smoother banking experience.
Benefit 2: Personalisation
AI can analyse vast amounts of customer data, including purchases and browsing habits, to create detailed customer profiles. These profiles help banks recommend relevant products and services that fit individual needs.
For instance, a customer who often pays bills online might be recommended a new budgeting tool. Similarly, someone who regularly saves for travel could receive information about travel insurance or currency exchange. These personalised suggestions can come through various channels, like the bank’s website, email alerts, or chatbots.
Benefit 3: Cost Savings
Cost savings are a major advantage of AI-powered customer service in banking. One key way AI achieves this is through automation. Chatbots powered by AI can handle many routine customer inquiries, freeing up human agents for complex issues. This reduces labour costs while also improving response times.
AI also helps with better staffing management. It can analyse past data to predict how many calls are coming in. Banks can then ensure they have the right number of agents available, avoiding overstaffing or understaffing that can significantly impact costs.
Benefit 4: 24/7 Support
Traditionally, reaching a support agent often meant waiting on hold during peak hours. However, AI in customer service is transforming the industry by offering immediate assistance through chatbots. These virtual assistants provide instant support the moment a customer reaches out.
Unlike human agents with limited working hours, chatbots are available 24/7. This ensures customers get help whenever they need it, regardless of location or time zone. This is especially valuable in the globalised world, where customers might need support outside of regular business hours.
A great example of this success is Photobucket, a media hosting service. After implementing a chatbot, they offered 24/7 support to international customers. This results in a three percent increase in customer satisfaction scores along with a 17 percent improvement in resolving issues on the first try.
Benefit 5: Multilingual Support
AI-powered chatbots offer multilingual support, breaking down language barriers and creating a positive banking experience. These chatbots can figure out a customer’s preferred language at the start of a conversation. This ensures clear communication, no matter what language the customer speaks.
Conclusion
A study by Global Market Insights predicts the conversational AI market will reach $57.2 billion by 2032. This technology is making big strides in banking, particularly by automating routine tasks and inquiries. By taking care of these repetitive tasks, AI frees up human agents to focus on more complex customer issues. This improves efficiency and helps banks manage their operating costs. A streamlined customer service experience builds trust and loyalty, which can lead to business growth for financial institutions.
McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption…
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McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption of the technology is helping the consulting titan and its peers boost revenue, across industries like Insurtech, following a period of tumult.
About 40 per cent of the New York-based firm’s client projects involve the technology. The number of AI-related customers in the past 12 months is approaching 500, Rodney Zemmel, senior partner and head of the firm’s digital business, said in an interview.
“We believe the long- or the medium-term economic implications are very real,” Zemmel said. He was a final candidate in the recent global managing partner leadership elections at the firm. According to people familiar with the matter, who asked not to be identified discussing confidential information.
Though there’s some degree of hype around AI, “we’re seeing the organisations that are doing that are getting value from it,” Zemmel said. “It’ll be a little longer, and maybe, a little harder than people think, but we’ve got no doubt that the value is there,” he added.
AI adoption across Insurtech
Among those deploying automation rapidly are the traditional and regulated industries such as banking and insurance, Zemmel said. In a June report, Citigroup Inc. said AI is poised to upend consumer finance and make workers more productive. Additionally, with a high potential for 54 per cent of jobs across banking to be automated. Citi also said that the technology could add $170 billion to the industry’s coffers by 2028.
JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon has called AI “critical” to his company’s future success. He also noted the technology can be used to help the firm develop new products, drive customer engagement, improve productivity and enhance risk management.
The surge in automation has come as a relief for the broader consulting industry. It has been battling a slowdown in demand for its traditional services. McKinsey, Ernst & Young and PricewaterhouseCoopers have been cutting jobs to weather the slump. Furthermore, Accenture Plc shares tumbled in March after the company warned it’s seen financial-services customers, including Insurtech, rein in spending on its software.
AI’s rise is also diverting some budgets toward specialist consultancies. Although AI-focused units like McKinsey’s QuantumBlack are growing rapidly, according to Zemmel.
McKinsey – QuantumBlack
McKinsey, which has advised everyone from the U.S.’ Pentagon to China’s Ping An Insurance Group Co., currently has about 2,000 people working across QuantumBlack. It has 7,000 staff in total in tech-related fields, according to Zemmel’s estimates. McKinsey’s headcount stood at about 45,000 globally as of 2023 and revenues were at a record $16 billion.
Zemmel said that the firm is still evaluating how the use of AI will impact its own headcount over the longer run. McKinsey had earlier warned about 3,000 of its consultants that their performance was unsatisfactory and will need to improve.
“We’re certainly planning on being agile about it,” Zemmel said. “One thing that’s clear is everybody in our organization’s going to need to know how to use AI and incorporate in their day-to-day work if they’re going to remain relevant to their clients.”
AI-powered threat detection, automation, and data analysis are empowering fintech cybersecurity teams to more effectively meet the challenges of an evolving world.
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Artificial intelligence (AI) is driving a new generation of modern cybersecurity solutions. The technology is transforming how organisations protect against evolving digital threats, as predictive and big data analytics bring new benefits to the sector.
How is AI transforming cybersecurity for fintech teams?
AI’s importance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, and adapt to evolving threats. It can also handle large amounts of data, making monitoring networks and detecting issues easier without increasing risks.
AI learns from past experiences, recognising patterns and improving over time. This makes it good at spotting weak passwords and alerting the right people. AI can also block harmful bots that try to overload websites. AI automates large amounts of tasks, allowing for 24/7 monitoring and quicker responses to security threats.
Its machine learning algorithms analyse vast datasets in real-time, identifying patterns and anomalies to detect emerging threats. As AI excels in behavioural analytics, it establishes a baseline of normal behaviour to spot deviations that indicate security threats.
Unlike traditional methods that rely on predefined signatures, AI can identify zero-day threats—new and previously unknown vulnerabilities—promptly. This proactive approach allows organisations to respond swiftly, preventing potential breaches before they occur.
AI also enhances threat intelligence by automating the analysis of code and network traffic, freeing up human analysts for more complex tasks. It, in turn, facilitates automated incident responses, rapidly mitigating attacks and minimising damage.
Predictive AI in Fraud Detection
AI is revolutionising fraud prevention by using predictive and behavioural analysis to detect and prevent fraudulent activities. By analysing historical data, AI identifies patterns that often precede fraud. This approach not only enhances detection accuracy but also reduces false alarms, distinguishing between normal and suspicious behaviours with greater precision.
In real-time, AI monitors multiple transactions simultaneously, flagging suspicious activities as they happen to mitigate risks promptly. It learns individual customer behaviours to detect anomalies, such as large transactions or unusual patterns. These triggers prompt alerts for investigation or automated protective measures, such as account freezing.
Despite challenges such as data privacy and the need for extensive datasets, AI’s advancements in machine learning promise increasingly effective solutions for protecting financial systems.
Industry case studies: Vectra and Kasisto
Fintech companies like Vectra use AI-powered technologies such as Cognito to automate threat detection and response. These systems analyse vast datasets to detect and pursue cyber threats swiftly, ensuring comprehensive security measures against malicious activities.
Tools like Kasisto’s KAI enhance customer experiences by providing personalised financial advice through AI-driven chatbots. This demonstrates AI’s versatile applications in improving both security and service delivery within the fintech sector.
AI’s use cases in cybersecurity are expected to increase. AI will revolutionise how users are authenticated. It will use advanced biometric analysis and behaviour tracking to make it harder for unauthorised users to gain access while ensuring a smooth experience for legitimate users.
This approach strengthens security by verifying identities with methods like fingerprints or facial recognition and detects unusual behaviours for added protection. AI’s ability to learn continuously from new data means cybersecurity systems will become smarter and more effective over time, adapting quickly to new threats.
The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…
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The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment, many are embracing artificial intelligence (AI) for assistance with investment decisions. AI acts as a powerful tool, improving efficiency and effectiveness across various aspects of asset management.
From analysing market trends to building diversified portfolios, AI’s strength lies in processing massive amounts of data. Furthermore, it uncovers hidden patterns empowering managers to make data-driven investment choices across financial services.
Introduction to AI in Asset Management
Asset management involves managing investment portfolios for individuals, institutions, and businesses. This includes stocks, bonds, real estate, and other financial assets. The main goal is to grow value over time while minimising risk and meeting client goals.
AI is transforming asset management with its data processing and analytics capabilities. Additionally, AI algorithms can quickly analyse massive amounts of financial data, market trends, and economic indicators. This helps uncover hidden patterns and connections that human analysts might miss. A data-driven approach empowers asset managers to make better investment decisions and develop more accurate market forecasts.
Portfolio Management
AI is transforming asset management by offering powerful tools for better decision-making. Moreover, machine learning (ML), AI analyses vast amounts of historical market data to identify patterns and predict future trends, providing valuable insights for building portfolios.
Natural language processing (NLP) lets computers understand human language. NLP can unlock information from unstructured sources like news articles, social media, and analyst reports. The algorithms then analyse sentiment and extract key information that feeds into portfolio decisions.
AI optimisation algorithms help construct optimal portfolios. These algorithms consider risk tolerance, return goals, and investment limitations. By using these tools, portfolio managers can create portfolios designed to maximise returns while minimising risk.
Risk Management
AI is changing how investment decisions are made. The AI algorithms can analyse massive amounts of historical market data and complex risk models.
The analysis provides a deeper understanding of individual asset risk and the overall portfolio’s exposure. With this knowledge, investment managers can proactively identify potential risks and develop strategies to lessen them.
AI offers real-time risk monitoring. An AI-powered system continuously tracks portfolio performance, alerting managers to any significant changes in risk. This allows for swift adjustments as market conditions evolve.
Automated Trading
Traditional automated trading tools execute trades based on pre-programmed instructions from human traders. These tools function within the parameters set by the user and can’t analyse markets on their own.
AI offers truly independent systems with tools that can analyse markets using technical and fundamental analysis with minimal human input.
AI uses sentiment analysis, ML, and complex algorithms to process vast amounts of information and identify trends. This data-driven approach removes the emotional bias that can affect human traders.
Case Studies
The asset management industry is seeing a rise in firms using AI to improve performance. A recent example isDeutsche Bank’s collaboration with NVIDIA. This multi-year project aims to integrate AI across their financial services. This includes virtual assistants for easier communication and AI-powered fraud detection. The bank expects faster risk assessments and improved portfolio optimisation.
Morgan Stanley is also making strides in AI adoption. Partnering with OpenAI, their financial advisors now have access to a massive research library at high speed. Advisors can explore client portfolio strategies and find relevant information in seconds, leading to better-informed advice.
Future Prospects
APwC report predicts artificial intelligence will significantly boost global GDP, contributing up to $15.7 trillion in 2030. This advancement could reshape asset management in the coming years, leading to entirely new business models and investment strategies.
One future possibility involves fully automated investment platforms powered by AI. These platforms would manage investment portfolios with minimal human involvement and use real-time data analysis to create personalised investment plans.
Moreover, AI could pave the way for more dynamic investment strategies that respond to market changes. By constantly analysing market conditions, AI can automatically adjust investment portfolios to optimise returns and minimise risks. This could lead to more resilient and adaptable investment systems that are better equipped to navigate various market environments.
Data analysis is critical for predicting risks and returns. The ever-growing size of data has overwhelmed human capacity. This is where artificial intelligence (AI) comes in.
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AI is transforming the financial sector by automating routine tasks and efficiently analysing large and complex data sets. It can analyse vast amounts of data with unprecedented speed. The instant but comprehensive insights that this capability provides lead to significantly improved accuracy.
Introduction to AI in Financial Forecasting
Financial forecasting can be challenging for smaller businesses. They often rely on assumptions and human judgement. This can result in inaccuracy, especially when unexpected events occur.
AI can analyse massive amounts of data to find hidden patterns that drive revenue. It automates routine tasks and enables a more detailed analysis than humans can achieve on their own.
Predictive Analytics
By automating data processing and interpretation, AI empowers financial teams to make informed decisions based on a strong analytical foundation. It goes beyond basic analysis by employing advanced algorithms and machine learning (ML) to extract valuable insights from data.
This not only improves the accuracy of forecasts but also unlocks a deeper understanding of market complexities that were previously out of reach.
Risk Assessment
AI algorithms use advanced data processing to spot patterns, unusual activities, and connections that traditional methods might miss.
By training ML models on past data, AI can learn to identify patterns associated with fraud. These models then analyse new transactions, compare features, and flag potential problems in real-time.
Real-time Data Analysis
Slow reporting and analysis have hindered companies’ ability to adapt. AI-powered systems overcome these issues by enabling real-time analysis and decision-making.
AI’s ability to process massive amounts of real-time market data helps financial experts identify opportunities and adapt to market shifts quickly. This translates to increased resilience and competitiveness for businesses.
Case Studies
Financial institutions are increasingly using AI to improve their forecasting and data analysis for managing operational risk. This trend is likely to continue as IndustryARC expects the AI market to reach US$400.9 billion by 2027, growing at a compound annual growth rate (CAGR) of 37.2% during the forecast period of 2022–2027.
Deutsche Bank‘s collaboration with NVIDIA on “Financial Transformers” shows the potential of AI for early risk detection. These models can identify warning signs in financial transactions and speed up data retrieval, helping banks address potential problems quickly and ensure data quality.
AI also plays a key role in anti-money laundering (AML) efforts. By analysing transaction patterns, customer behaviour, and risk indicators, AI can identify suspicious activities for investigation. This not only improves detection rates but also streamlines the process. Google Cloud’s AML AI is a prime example; it helped HSBC find many more real risks while significantly reducing false positives, saving them time and resources.
Future Prospects
AI in finance is expected to significantly reshape financial forecasting. Analysts and executives will see widespread AI adoption for tasks like data analysis, pattern recognition, and automation. This trend is driven by the projected growth of global AI in the finance market. A report by Research and Markets predicts it will reach $26.67 billion by 2026, growing at a rate of 23.1% each year.
For investment firms, AI can make highly accurate forecasts and execute complex trading strategies, optimising investment decisions and returns. Banks will also benefit from AI’s capabilities. AI-powered data analysis can give banks a deeper understanding of their customers, enabling personalised financial services. Chatbots and robo-advisors used for customer service and financial planning will continue to evolve, becoming more advanced and even more human-like in their interactions.
Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…
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Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new heights, including in the insurance industry.
Financial technology development has offered a better customer experience with enhanced accessibility and convenience. Mobile banks and digital wallets make it possible to contact the customer service team through online platforms. With the help of AI, FinTech companies escalate their services by offering more personalised, prompt, and efficient service.
AI Chatbots and Virtual Assistants
Conversational AI, which focuses on creating human-like interactions like chatbots and virtual assistants, improves customer service efficiency.
Chatbots are automated programmes that promptly address customer service queries. They can assist customers with inquiries and provide support for product information, account balances, or transaction details. AI-powered chatbots can give an immediate response and handle multiple customers at the same time.
Meanwhile, virtual assistants are voice-activated apps that can comprehend and carry out tasks based on users’ commands. These assistants offer personalised support by understanding the customers’ needs. For instance, they can deliver investment guidance tailored to customers’ risk tolerance and financial objectives.
These AI solutions can also assist human assistants by handling routine tasks, allowing them to focus on more complex work. Thus, the employment of AI assistants can reduce operational costs and effectively allocate resources to more important tasks.
Personalised interactions with AI
This approach can provide more personalised interactions by using algorithms and predictive tools to understand and respond to each customer’s preferences. AI algorithms can analyse large datasets of customers’ past interactions, browsing behaviour, and demographic information.
Meanwhile, predictive analytics tools can be used to anticipate customer needs and offer relevant financial products or services. These recommendations are constantly updated based on real-time client interactions and feedback.
24/7 Support
AI-powered customer service has the benefit of around-the-clock availability. It can operate continuously without being bound by office working hours like human-based customer service. Faster response times and enhanced availability help FinTech companies improve overall customer satisfaction.
Case Studies
Paypal, a digital wallet company, is one of the FinTech companies that has successfully used AI to improve its customer service. After implementing chatbots, PayPal experienced a 20 percent decrease in customer support costs and a 25 percent increase in user engagement. These chatbots can handle routine inquiries, resolve issues, and make personalised product recommendations.
Another example is Citi, a US retail bank that developed an AI-powered Customer Analytic Record (CAR). This programme can consolidate customer data, including financial records, used products, and interactions across online banking. The data is linked to automated decision-making AI software for analysis. The system can then recommend personalised offers to customers via text and mobile banking.
Future prospects
According to David Griffiths, Citigroup’s chief technology officer, AI has the potential to revolutionise the banking industry and improve profitability. With the continuous development of AI technology, the fintech industry can further improve its customer service.
Ronit Ghose, another executive at Citigroup, predicts that in the future, every client will have an AI-powered device in their pocket. This innovation will improve their financial lives with enhanced AI in customer service.
However, there are still concerns about AI’s scalability limitations in handling vast amounts of tasks. In addition, AI’s access to customers’ data makes security an important area to ensure its credibility. FinTech companies should ensure digital compliance to earn customers’ trust.
The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…
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The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer experience to automating menial tasks. However, many are still cautious about using AI in certain areas, such as regulatory compliance management.
Given the continuously evolving legal requirements, good regulatory compliance management is crucial for banks. AI solutions can help effectively manage compliance by automating repetitive tasks, detecting suspicious activity, and providing real-time insights.
Automated compliance monitoring with AI
Artificial intelligence allows banks to perform continuous tasks around the clock with automated compliance monitoring. The previously labour-intensive work can be done more efficiently to ensure the bank follows all regulatory obligations.
The bank’s compliance teams usually handle monitoring processes, but AI automation can reduce costs. The compliance team can also focus on more important tasks rather than repetitive work.
The increased efficiency also means reduced compliance risk and non-compliance damage like fines.
Risk management
Financial institutions face regulatory compliance risks in various areas, which can lead to legal sanctions, financial loss, or a bad reputation. Advanced AI solutions can aid in risk management by identifying and mitigating risks more effectively.
AI-powered solutions can develop more accurate risk models and provide real-time responses. Many banks use this technology to help streamline compliance while improving the security of sensitive financial data. Furthermore, AI can detect compliance gaps and ensure adherence to laws and regulations.
Data analysis
AI can quickly analyse large volumes of data, a novel capability in the industry. A data analysis system can be designed to keep track of the latest regulatory changes and ensure the bank remains compliant.
Machine learning models can identify suspicious patterns and detect anomalies to report any breach of regulation. They can also analyse historical data and predict compliance risks. These allow banks to mitigate risks and address compliance issues before they escalate.
Case studies
Several banks have successfully used AI for regulatory compliance solutions.HSBC, for instance, uses AI-powered Know Your Customer (KYC) verification. This system can analyse customer data quickly, identify potential risks, and alert compliance officers for investigation.This bank also used Google Cloud’s Anti Money Laundering (AML) AI to combat and detect fraudulent activities in real-time. With these, HSBC has reduced the verification time by 80 percent and experienced a significant reduction in false positives.
Meanwhile,Danske Bank has also earned benefits from using fraud detection AI. The bank witnessed a 60 percent reduction in false positives and a notable decrease in fraudulent activities.
Future outlook for AI in regulatory in compliance
AI solutions are predicted to fundamentally change financial institution compliance management in the next five years, according to McKinsey. In the future, implementation for regulatory compliance in banks will be more widespread. Over 80 percent of C-level executives who participated in an Accenture survey planned to commit 10 percent of their AI budget by 2024 to address regulatory compliance.
AI offers many benefits, and as accessibility to this financial technology increases, more financial institutions will be inclined to adopt it, according to theFinancial Stability Review.
Technology will evolve, giving better automation capabilities, more extensive data analysis, and enhanced interpretation. This could further reduce the manual effort required in the banking industry.
As adoption increases, ensuring the AI systems used are ethical and unbiased is necessary. Thus, banks need to provide transparency for AI in banking and adherence to guidelines.
FinTech Strategy and Interface joined Publicis Sapient at Money20/20 in Amsterdam for the launch of its third annual Global Banking Benchmark Survey and spoke with Head of Financial Services Dave Murphy about its findings
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The third annual Global Banking Benchmark Study from Publicis Sapient draws on insights from 1000+ senior executives in financial services across global markets. The study focuses on the goals, obstacles, and drivers of digital transformation in banking.
Global Banking Benchmark Study
The study was launched during Money20/20 Europe in Amsterdam last month. Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted the banking industry is focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation. Additionally, there is a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by the banking industry. These include regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure. Also, building cloud infrastructure to support ongoing digital transformation. Moreover, the study notes the prevalence of the development of custom-made tools and internal use cases for AI implementation. Furthermore, Eoghan and Grace provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
Four key takeaways from Publicis Sapient
Four key tracks came out of the study…
Modernising the core will always be important. But modernising the core for its own sake and also building the cloud infrastructure that supports it or allows for it to be modern. A decent chunk of the survey responders are still very focused on this. Executives are stating they want to make sure their people can make the best use of the beautiful core they’ve now built.
GenAI is an area of thoughtful experimentation for the Neobanks. We’re talking about scaled microservices here. Instances where, across Neobanks, you’ll have the same machine learning model and the same GenAI text generator facilitating retail and SMEs. That’s pretty sophisticated and something everyone has to contend with.
Data Analytics transformation is a key priority using GenAI to do so along with bringing new talent into the game.
Payments has been a big theme at Money20/20… We’re seeing lots of activity around ancillary individual product areas.
“The study focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models. AI is finally delivering a set of use cases that truly can impact the way banks operate and build their own technology.” Dave Murphy, Head of Financial Services, EMEA & APAC
Be among the first to receive the study by signing up here
The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference.
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Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities. These underscored the transformative power of emerging technologies in financial services.
This year’s theme was ‘Human X Machine’. Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…
Innovations in AI and Open Banking
Artificial Intelligence was a major theme throughout Money20/20. A notable session featured Patrice Amann from Microsoft and Kevin Levitt from NVIDIA. They discussed the role of Generative AI in transforming customer experiences in banking. They highlighted the importance of integrating business-specific data to enhance the accuracy and effectiveness of AI solutions.
Open banking also garnered significant attention at Money20/20. Mastercard and bunq announced a partnership enabling users to consolidate multiple bank accounts through bunq’s AI-driven money assistant, Finn. This move is part of a broader trend towards greater financial integration and personalised banking experiences. Additionally, Token.io and Prommt unveiled a collaboration to improve open banking payments. This illustrated the increasing importance of seamless, user-friendly payment solutions in the fintech landscape.
Michelle Prance, CEO, Mettle (NatWest Group): “It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank for fintechs. Quite often their route to market, and capitalisation, is by going into a main bank being acquired. It’s a marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”
Episode Six
Craig Ramsay, MD Business Development, Episode Six: “Networking is really important for us as a small company. There are lots of people here who can actually solve problems and it’s the collaboration I get quite excited about. What I’ve seen change in recent years is that the big banks are looking to find small organisations like us to figure out how to solve their payments problems. And that’s different to when I was working for a bank only a few years ago. You just have to be here at Money20/20… What I’m seeing, since we returned after Covid, is how many people from different parts of the world are coming here to actually talk to each other in person. If you’re not here at Money20/20, then it’s actually hard to be relevant in this industry.”
