Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes

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 Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.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.

  • Artificial Intelligence in FinTech
  • Neobanking

Glenn Fratangelo, Head of Fraud Product Marketing & Strategy at NICE Actimize, on financial services fraud prevention in 2025.

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.

  • Artificial Intelligence in FinTech

Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking

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.

  • Artificial Intelligence in FinTech

additiv, a global leader in fintech and digital transformation, has announced the launch of an InsurTech solution with AXA Switzerland

AXA Switzerland has successfully launched its addProtect bancassurance offering, powered by additiv’s technology platform. Furthermore, this innovative InsurTech solution allows banks to directly protect their mortgage customers against key risks with a simple plug-and-play solution.

addProtect InsurTech solution from additiv

As a seamless plug-and-play solution, addProtect gives banks direct access to the platform without the need for additional integration with existing IT systems. Its user-friendly and intuitive design allows banks to effortlessly integrate the platform into their day-to-day business operations. With the death and payment protection insurance, bank advisors have easy-to-understand products at their disposal. These offer added value to customers beyond the existing offering. The addProtect platform is now available for banks, and an initial pilot will be launched in collaboration with PostFinance.

Samuel Peter, Head of Partnerships at AXA Switzerland, stated:

“With addProtect, AXA is responding to the growing need of customers and banks for appropriate insurance solutions where and when they are needed. The solution creates additional advisory potential and better protection for the customers of our partners’ banks. We look forward to making the solution available to other partners.”

Dieter Lützelschwab, General Manager Switzerland at additiv, added:  

“When developing addProtect, we focused on the user experience for the customer and the bank advisor. In addition, our platform provides an easily configurable, modular insurance solution that covers the entire value chain from quotation to claims processing.”

About additiv

additiv empowers the world’s leading financial institutions and brands to create new business models and transform existing ones. additiv’s API-first cloud platform is one of the world’s most powerful solutions for wealth management, banking, credit, and insurance. The InsurTech technology, together with the global ecosystem of regulated financial services providers, opens up new opportunities for banks, insurance companies, asset managers, IFAs and consumer brands to quickly and flexibly offer their own and third-party financial solutions through existing or new customer channels.

Headquartered in Switzerland, with regional offices in Singapore, UAE, Germany, and the UK, and more than 250 employees, additiv serves over 400 financial institutions (banks, insurers, asset managers, pension providers, IFAs, etc.) and brands worldwide.

  • InsurTech

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

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.

  • Artificial Intelligence in FinTech

Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors

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.

  • Artificial Intelligence in FinTech

Gabe Hopkins, Chief Product Officer at Ripjar, on how GenAI can transform compliance

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.

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech

The AXA Group aims to protect over 20 million customers through inclusive insurance globally by 2026

AXA Egypt and Post for Investment (PFI), the investment arm of Egypt Post, are establishing the first micro-insurance company in Egypt. This strategic collaboration is made possible by leveraging the new insurance law and aims to revolutionise the insurance landscape in the country.

Financial Inclusion

This initiative is fully aligned with AXA´s conviction that postal networks play a crucial role in global financial inclusion. Over a quarter of the world’s adult population accesses formal financial services through their post office. AXA notably signed a partnership with the Universal Postal Union (UPU) in May 2024. Moreover, this collaboration with UPU includes a research program. It will showcase successful postal insurance models and the establishment of the Postal Insurance Technical Assistance Facility (PITAF). This will promote financial inclusion and risk mitigation among underserved populations. Through this partnership, AXA is pushing the boundaries of insurance to better protect all. Solidifying its dedication to inclusive insurance practices worldwide.

The Egypt Post, who will be the main distribution channel of this JV, is a well-respected organisation. It has a strong nationwide presence, renowned for its last mile distribution capabilities and robust brand credibility. Additionally, with over 4000 branches, kiosks, and mobile trucks across all governorates, Egypt Post is an integral part of the country’s infrastructure. It caters to the population with unparalleled reach.

“We believe in the power of collaboration to create lasting change, and this joint venture is a testament to our commitment to inclusive insurance. Together, we are revolutionising the insurance landscape in Egypt to better protect and empower communities, setting new benchmarks for millions seeking reliable and accessible insurance protection.”

Garance Wattez-Richard

Micro-insurance from AXA

The product categories will include both retail and group offerings. Embedded and voluntary options will cater to diverse needs. The range of products will cover various areas. These include hospital cash, personal accident, term life, payment protection, credit life, livestock, and group protection, ensuring comprehensive coverage for the customers.

The ambitious goal is to reach 12 million customers within the first decade of operation. Therefore, underlining the commitment to making a significant impact on the lives of Egyptians through tailored insurance solutions.

This collaboration between AXA EssentiALL, AXA Egypt and PFI/Egypt Post marks a significant milestone in the local insurance industry. It paves the way for inclusive and impactful micro-insurance offerings that have the potential to transform the socio-economic landscape of Egypt. As the first of its kind, this micro-insurance company is poised to set new benchmarks. Furthermore, it can become a beacon of hope for millions of Egyptians seeking reliable and accessible insurance protection.

  • InsurTech

FinTech Strategy spoke with Ryan O’Holleran, Head of Sales, Enterprise, EMEA at Airwallex, to learn about the global payments and financial infrastructure provider

Airwallex, a financial infrastructure provider, offers a range of services. Including multicurrency accounts, payment acceptance card issuing, foreign exchange (FX) payouts, treasury and expense management. In addition to supporting small and medium-sized businesses, the company also provides APIs and a software layer for direct access to enterprise businesses. As well as an enterprise platform product called Scale. Airwallex has found success working across various industries. It works with the likes of Bird (formerly MessageBird) to handle global accounts and backend treasury, and partners with Qantas to offer financial tools for their business partners.

The company also enables faster and more efficient payments through its patchwork network of financial partnerships and licenses. Airwallex has experienced significant growth even during economic downturns. As of August this year, Airwallex globally processed transactions worth more than $100 billion annually and saw a 73 percent year-on-year increase. It is now focused on embedded finance solutions and global expansion.

At Money20/20 Europe, FinTech Strategy spoke with Airwallex’s Head of Sales, Enterprise, EMEA, Ryan O’Holleran, to find out more…

Tell us about the genesis of Airwallex?

“Our co-founder, Jack Zhang, started a coffee company in Melbourne, Australia, which is still around today, with a few friends from university. And while they were building out this coffee shop, they were buying beans from abroad, along with supplies and packaging. They found how hard it was to actually pay for services, send funds abroad and deal with multiple currencies. So, they saw an opportunity to help streamline the financial infrastructure for small businesses. That’s when Jack and his co-founders put Airwallex together and built out an initial SME’s use case to allow multicurrency accounts and FX payouts. Since then, the business has really expanded…

Today, Airwallex provides a set of APIs – we’re really providing financial infrastructure to move money globally. On those APIs, we also have a layer of software that we can offer direct access to enterprise businesses. The third part of this, which is kind of the new product over the last three years, is our enterprise platform product called Scale. Scale allows you to embed those financial services into a product as well as a platform or marketplace. So, you kind of think about it as a direct treasury product, APIs and a platform product.”

Tell us about your role at Airwallex?

“I’m originally from San Francisco, grew up in the Bay area, started in tech, did a couple of startups, and I actually got into payments via Stripe. I joined Stripe back when there were about 200 employees in San Francisco. Spent some time in Chicago and then moved to the UK initially with Stripe. I was there for about five and a half years, as we went from 200 staff to 6,000. At that point, I wanted to get back to something a little bit different. To help more cross-functioning with product and help scale businesses. The opportunity with Airwallex came along where I saw the company addressing many things my customers at Stripe were asking for.

So, the FX piece, mass payouts, treasury, all complimented what Stripe is doing with acquiring. Since I joined the team three years ago, we’ve been scaling across EMEA. We now have offices in London, Amsterdam, Vilnius and just last year launched our office in Tel Aviv to cover Israel. And we have teams in the Americas and APAC where Airwallex was founded.”

What are some of the key challenges financial institutions are facing that you can help them with? What problems are companies asking you to solve? In doing so, what are the challenges for Airwallex?

“We work in different areas. This is where I think we have differentiated the business and also where I see the industry moving. If you look back over the last five, 10 years, there was this approach where you had Stripe and all the major players coming in and saying, we can do things and we can do it really well and you only need to use us, you don’t need to use a patchwork of providers. I think that is starting to shift. You see this with orchestration layers like Primer or Gravy, allowing people to be agnostic on PSPs. And then you’re seeing people think about redundancy. So, the heads of payments we’re talking to this week are looking at two or three providers because they need redundancy or want to use the best provider in each region. They don’t want to be siloed.

Airwallex can be used in a segmented approach. So, if you just need us for payouts, you can do that. If you just need us for FX, you can do that. If you just need us for acquiring, you can do that. Or we could do that globally and you can adjust as you see fit. So, the flexibility of Airwallex I think is one of our superpowers.”

Tell us about some of the successful partnerships Airwallex has been involved in…

“The interesting thing about Airwallex is that since we’re providing financial infrastructure, there’s a huge variety of customers we work with. One of the local ones is Bird (a cloud communications platform that connects enterprises to their global customers). Using our software product they are creating global accounts, handling backend treasury, payroll, suppliers and more. We’ve also worked with Qantas to build out an SMB solution embedding all of the Airwallex financial services and they call it Qantas Business Money.          

Elsewhere, Brex in the US were looking for a provider to help with their payout rails. One of the things Airwallex has done is rebuilt the Swift network via local rails. So, we have a patchwork network of financial partnerships and licences where if you are located, let’s say in the US, but you want to pay somebody out in the UK, you get access to faster payment rails having never set foot in the UK or separate rails via Europe having never set foot in the EU. So, you get this mass payoff solution of local rails, which is faster, cheaper, and more efficient than using something like Swift.”

“I think where we’re seeing a lot of opportunities, in EMEA specifically, in B2B, vertical, SaaS, travel and marketplaces, is this embedded finance solution. It was kind of a buzzword a few years ago and now we’re actually starting to see it develop. I view it as actually embedding all of these financial services – whether it be a wallet, issued cards, or local multi-currency accounts – and being able to monetize that. So, we’re seeing this with a lot of our customers actually wanting to white label our products, embed that and bring payments on platform.”

And what’s next for Airwallex? What future launches and initiatives are you particularly excited about?

“The growth of Airwallex, specifically on a global scale, over the last few years is one thing I’m very proud of because it’s happened during one of the worst economic downturns we’ve experienced. FinTech was almost retracting in terms of budgets and investments. You’re starting to see the tide turn, but we were able to grow over 100 percent year on year, through some of the toughest times for business. And now we’re really starting to see that pick up because the businesses, who actually decided this is going to be a building year for us now, they’re going live, they’re accelerating, they’re growing.

And so we’re seeing the ROI of that investment. It’s a testament to the global financial infrastructure we’ve built. Meanwhile, Airwallex became cash flow positive in 2023. It now processes more than $100 billion in annualised transaction volume. The company now employs over 1,500 people worldwide working across 23 international offices.”

Why Money20/20? What is it about this particular event that makes it the perfect place to showcase what you do? How has the response been to Airwallex?

“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, etc… And then you have the Heads of Payments from companies like Booking.com, Vinted and SumUp who are coming here with their teams 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 that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations. Three years ago, we first came to suss out the event and as we’ve grown the response has grown. People are being proactive and keen to engage with us which is exciting to see.”

  • Digital Payments
  • Embedded Finance

Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services

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.”

Darren Nisbet, CEO, Virtuoso

  • Artificial Intelligence in FinTech

Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services

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.

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech

Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech

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.

  • Artificial Intelligence in FinTech

Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?

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:

  1. 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.
  2. 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:
  3. AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
  4. 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?

AI will transform payment data analysis

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.

Quicker, more accurate invoice reconciliation

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.

Adflex has 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.”

  • Artificial Intelligence in FinTech
  • Digital Payments

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

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.

  • Artificial Intelligence in FinTech

Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…

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.

  • Artificial Intelligence in FinTech

Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…

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.

  • Artificial Intelligence in FinTech

The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…

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 is Deutsche 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

A PwC 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.

  • Artificial Intelligence in FinTech

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.

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.

  • Artificial Intelligence in FinTech

Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…

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.

  • Artificial Intelligence in FinTech

The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…

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 the Financial 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.

  • Artificial Intelligence in FinTech

Satya Mishra, Director, Product Management at Amazon Business, discusses how CPOs have become an important voice at the table to drive digital transformation and efficient collaboration.

Harnessing efficiency is at the heart of any digital transformation journey.

Digitalisation should revolve around driving efficiency and achieving cost savings. Otherwise, why do it?

Amazon is no stranger to simplifying shopping for its customers. It is why Amazon has become a global leader in e-commerce. But, business-to-business customers can have different needs than traditional consumers, which is what led to the birth of Amazon Business in 2015. Amazon Business simplifies procurement processes, and one of the key ways it does this is by integrating with third-party systems to drive efficiencies and quickly discover insights. 

Satya Mishra, Director, Product Management at Amazon Business, tells us all about how the organisation is helping procurement leaders to integrate their systems to lead to time and money savings.

Satya Mishra: “More than six million customers around the world tap Amazon Business to access business-only pricing and selection, purchasing system integrations, a curated site experience, Business Prime, single or multi-user business accounts, and dedicated customer support, among other benefits.

“I lead Amazon Business’ integrations tech team, which builds integrations with third-party e-procurement, expense management, e-sourcing and idP systems. We also build APIs for our customers that either they or the third-party system integrators can use to create solutions that meet customers’ procurement needs. Integrations can allow business buyers to create connected buying journeys, which we call smart business buying journeys. 

“If a customer does not have existing procurement systems they’d like to integrate, they can take advantage of other native tools, like a Business Analytics dashboard, in the Amazon Business store, so they can monitor their business spend. They can also discover and use some third-party integrated apps in the new Amazon Business App Center.”

Why would a customer choose to integrate their systems? Are CPOs leading the way?

Satya Mishra: “By integrating systems, customers can save time and money, drive compliance, spend visibility, and gain clearer insights. I talk to CPOs frequently to learn about their pain points. I often hear from these leaders that it can be tough for procurement teams to manage or create purchasing policies. This is especially if they have a high volume of purchases coming in from employees across their whole organisation, with a small group of employees, or even one employee, manually reviewing and reconciling. Integrations can automate these processes and help create a more intuitive buying experience across systems.

“Procurement is a strategic business function. It’s data-driven and measurable. CPOs manage the business buying, and the business buying can directly impact an organisation’s bottom line. If procurement tools don’t automatically connect to a source of supply, business buying decisions can become more complex. Properly integrated technology systems can help solve these issues for procurement leaders.”

Satya Mishra, Director, Product Management at Amazon Business

Beyond process complexity, what other challenges are procurement leaders facing?

Satya Mishra: “In the Amazon Business 2024 State of Procurement Report, other top challenges respondents reported were having access to a wide range of sellers and products that meet their needs, and ensuring compliance with spend policies. 

“The report also found that 52% of procurement decision-makers are responsible for making purchases for multiple locations. Of that group, 57% make purchases for multiple countries.

“During my conversations with CPOs, I hear them say that having access to millions of products across many categories through Amazon Business has allowed them to streamline their supplier quantity and reduced time spent going to physical stores or trying to find products they’re looking for from a range of online websites. They’ve also shared that the ability to ship purchases from Amazon Business to multiple addresses has been very helpful in reducing complexity for both spot-buy and planned or recurring purchases. Organisations may need to buy specific products, like copy paper or snacks, in a recurring way. They may need to buy something else, like desks, only once, and in bulk, at that. Amazon Business’ ordering capabilities are agile and can lessen the purchasing complexity.”

How should procurement leaders choose which integrations will help them the most? 

Satya Mishra: “At Amazon Business, we work backwards from customer problems to find solutions. I recommend CPOs think about what existing systems their employees may already use, the organisation’s buying needs, and their buyers’ typical purchasing behaviors. The buying experience should be intuitive and delightful. 

“Amazon Business integrates with more than 300 systems, like Coupa, SAP Ariba, Okta, Fairmarkit, and Intuit Quickbooks, to name just a handful. With e-procurement integrations like Punchout and Integrated Search, customers start their buying journey in their e-procurement system. With Punch-in, they start on the Amazon Business website, then punch into their e-procurement system. With SSO, customers can use their existing employee credentials. Our collection of APIs can help customers customise their procure-to-pay and source-to-settle operations. This includes automating receipts in expense management systems and track progress toward spending goals. 

“My team recently launched an App Center where customers can discover third-party apps spanning Accounting Management, Rewards & Recognition, Expense Management, Integrated Shopping and Inventory Management categories. We’ll continue to add more apps over time to help simplify the integrated app discovery process for customers.

“Some customers choose to stack their integrations, while others stick with one integration that serves their needs. There are many possibilities, and you don’t just have to choose one integration. You can start with Punchout and e-invoicing, for example, and then also integrate with Integrated Search, so your buyers can search the Amazon Business catalog within the e-procurement system your organisation uses.”

Are integrations tech projects?

Satya Mishra: “No, integrations should not be viewed as tech projects to be decided by only an IT team. Integrations open doors to greater data connectivity and business efficiencies across organisations. Instead of having disjointed data streams, you can connect those systems and centralise data, increasing spend visibility. You may be able to spot patterns and identify cost savings that may have gotten lost otherwise. 

“It’s not uncommon for me to hear that CPOs, CFOs and CIOs are collaborating on business decisions that will save them all time and meet shared goals, and integrations are in their mix of recommendations. 

“One of my team’s key goals has been to simplify integrations and bring in more self-service solutions. In terms of set-up, some integrations like SSO can be self-serviced by the customer. Amazon Business can help customers with the set-up process for integrations as well.”

How has procurement transformed in recent years?

Satya Mishra: “Procurement is no longer viewed as a back-office function. CPOs more commonly have a seat at the table for strategic cross-functional decisions with CFOs and CIOs.

“95% of Amazon Business 2024 State of Procurement Report respondents say the purchases they make mostly fall into managed spend. Managed spending is often planned for months or years ahead of time. This can create a great opportunity to recruit other stakeholders across departments versus outsourcing purchasing responsibilities. Equipping domain experts to support routine purchasing activities allows procurement to uplevel its focus and take on higher priorities across the organisation, while still maintaining oversight of overarching buying patterns. It’s also worth noting that by connecting to e-procurement and expense management systems, integrations provide easy and secure access to products on Amazon Business and help facilitate managed spend.”

What does the future of procurement look like?

Satya Mishra: “Bright! By embracing digital transformation and artificial intelligence to form more agile and strategic operations, CPOs can influence the ways their organisations innovate and adapt to change.”

Read the latest CPOstrategy here!

Nigel Greatorex, Global Industry Manager at ABB, on how digital technologies can support decarbonisation and net zero goals

Nigel Greatorex is the Global Industry Manager for Carbon Capture and Storage (CCS) at ABB Energy Industries. He explains how digital technologies can play a critical role in the transition to a low carbon world by enabling global emissions reductions. Furthermore, he highlights the role of CCS and how challenges can be overcome through digitalisation.

Meeting our global decarbonisation goals is arguably the most pressing challenge facing humanity. Moreover, solving this requires concerted global action. However, there is no silver bullet to the global warming crisis. The solution requires a mix of investment, legislation and, importantly, innovative digital technologies.

Decarbonisation digital technologies

It’s widely recognised decarbonisation is essential to achieving net zero emissions by 2050. Decarbonisation technology is becoming an increasingly important, rapidly growing market. It is especially relevant for heavy industries – such as chemicals, cement and steel. These account for 70 percent of industrial CO2 emissions; equal to approximately six billion tons annually.

CCS digital technologies are increasingly seen as key to helping industries decarbonise their operations. Reaching our net zero targets requires industry uptake of CCS to grow 120-fold by 2050, according to analysis from McKinsey & Company. Indeed, if successful, it could be responsible for reducing CO2 emissions from the industrial sector by 45 percent.

A Digital Twin solution

ABB and Pace CCS joined forces to deliver a digital twin solution. It reduces the cost of integrating CCS into new and existing industrial operations. Simulating the design stage and test scenarios to deliver proof of concept gives customers peace of mind. Indeed, system designs need to be fit for purpose. Also, it demonstrates the smooth transition into CCS operations. Additionally, the digital twin models the full value chain of a CCS system.

Read the full story here

  • Sustainability Technology

In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau…

In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau to regulate indemnity hospital insurance plans offered to individuals, with voluntary participation by insurance companies and consumers. The VHIS was designed as a means of encouraging and supporting customers to purchase private healthcare services and for Koh Yi Mien, Managing Director Health and Employee Benefits at AXA Hong Kong, this scheme represents a broader transformation of healthcare and insurance services. “Currently, the demand on healthcare in Hong Kong in the public sector is incredibly high with very long waiting times and waiting lists,” she explains. “As a result, people just aren’t getting timely access to treatment. The private sector in Hong Kong, which is world-class, has capacity. So, if we can rebalance and shift some of the elective work from public to private, it will free up more people to use the public service in a timely fashion.”

Yi Mien also points to a global drive for greater transparency, accountability, use of data and technology as well as promoting customer choice as key drivers of change in the insurance space. “It’s no longer a case of simply providing reimbursement to people when they need treatment,” she says. “It’s about being the patient’s partner throughout their whole life so that when they need healthcare, whenever and wherever they are, we are there to help and support them in their times of need.” 

The modern-day insurance customer is very different from the customer of the past. We live in times of greater access to information through the advent of social media and the increasing influence of the Internet and this has resulted in insurance customers being more knowledgeable about their conditions and asking more questions of their doctors than ever before. As a result, the balance between the customer and the healthcare provider is becoming more equitable. “Customers and patients, as a result, are becoming more demanding,” says Yi Mien. “Gone are the traditional ideas that doctor knows best. It’s not uncommon for patients to see their doctor with a list of demands, while expecting to be serviced.”

Running parallel to becoming more knowledgeable and demanding is the use of smartphones and how it has created a culture of service in an instant. When customers purchase etiquettes or use banking services, they expect the ability to be able to access and complete these transactions and services via their smartphone devices. Fewer and fewer people are accessing physical bank branches and the healthcare insurance sector, despite being still very traditional, is feeling the effects of this instant demand. “Healthcare is a very traditional sector sure, but asking patients or customers to book weeks in advance and telling them they don’t really have any choice is becoming increasingly unacceptable and so healthcare becomes a commodity,” says Mie Koh. “They, like any other customer, vote with their feet and want 24/7 access to quality healthcare without waiting directly from us as the insurer.”

The informed customer and patient have also transformed the relationship between customer and doctor. It is no longer a bilateral relationship and the entire healthcare ecosystem works to provide services from prevention right through to treatment. The result? Insurers like AXA work with customers before they are sick and encourage them to maintain their health, but they also work with clients during their illness and even afterwards AXA will continue to treat them in their rehabilitation. “During their healthcare journey, customers want some handholding in order to navigate the very complex healthcare system, to make sure they get the right healthcare provider, doctor and hospitals that are best for them in their time of need,” says Yi Mien. “This can only happen if we are using digital so that it becomes more real time.”

AXA has been embracing technology for a number of years to be able to serve and effectively work with its customers. It achieves this by starting with the definition of a product, because the product sets the rules. Yi Mien highlights that the rules would often be how AXA would spell out the terms and conditions, the provisions, but these rules also set the customer expectations. Throughout late 2018 and 2019, AXA has invested in digital to enable its customers to buy online, service online, claim online and check-up online. The company also launched a servicing app called Emma, a ‘digital companion’ that enables even faster service. Yi Mien describes this app as a true “health companion”. She is also keen to highlight that the technology is only part of the story. AXA has built a vast medical network with some of the leading hospitals and doctors and customers simply having to log into their companion app to be able to access this network at the touch of a button. “All they need to show is their digital card, their e-card, and with the QR code, the provider just scans it. All of the data is downloaded and all they need to do is sign, get their treatment, and then when they discharge, just sign that they have received the treatment and off they go,” she says. “The hospital will bill AXA directly so there’s no out of pocket. The data is also transmitted to AXA which means that we have more comprehensive and more reliable data.”

Comprehensive and reliable data is crucial to the technology journey of AXA, but it is also integral to the customer journey. With a customer’s entire electronic medical records stored effectively and securely, as Yi Mien notes, why would they go anywhere else? The data that an insurer handles is often complex in nature, but this data is processed through artificial intelligence, with AI being used to process claims more effectively and interpret the information to allow AXA to create rules and algorithms to better serve its customers. AXA also utilises AI through its companion app Emma. “Emma is our chatbot,” explains Yi Mien. “Emma has been built up based on a multitude of Q&As that our customer services team have recorded and collected over many months and years. As we continue to build, and more people use Emma, then the quality of the responses she has in her arsenal will improve.” In the first two months of operations, Emma recorded an accuracy level of 50%. Yi Mien firmly believes that as more people engage with Emma and as a result, the chatbot will evolve and become more of a real-time navigator that can direct customers across the whole ecosystem.

In the global discussion around AI, the topic of transparency is often a key point of debate. With governments around the world shining a spotlight on exactly what data is collected and how it is used, AXA ensures that it maintains an open and transparent dialogue with its customers. As customers engage with Emma and the companion app, they can at any time request their transcripts. Should they choose to speak with a human adviser, all calls are recorded and again they can access those recordings should they wish. Not only is this an example of AXA complying with global governing laws, it also highlights that the customer is at the very heart of every decision it makes and it maintains this as it continues to implement new technologies. “If you look at banking as an example, we all are so used to accessing our bank accounts at any time, be it through our phones or online,” says Yi Mien. “If we want to speak to someone, we can. If we want to go into a branch, we can. I believe this is the way to go with insurance as well. We make it easy for our customers to contact us. We are doing everything we can to allow that.”

“Healthcare is quite personal, so we are doing what we can to allow customers to speak to people, should they not wish to use our chatbot. These are very personal journeys and digital is still in its early days, so we really have to provide different avenues and channels for our customers to contact us.”

As Yi Mien notes, AXA designs its customer journey by starting at the product and going through all the way to treatment. The company makes every decision with the customer’s perspective in mind. As a doctor by trade, Yi Mien sees that all new products are designed by doctors because they understand how the patients move throughout the whole healthcare ecosystem. When AXA designs new products, it does not operate within a vacuum. It has a customer insight group, where around 1,000 customers operate as a real-time focus group in which AXA can test its products with. “When I think about future products, we will test with this group of people and get feedback to see whether we are aligned with the current customer need. So, it’s not just technology per se, but actually meets a customer’s needs,” she says. “One other area to make sure that we are doing the right thing, because technology also costs money, is to make sure that we are very robust in what we do. AXA is unique in that we sell life insurance, health insurance, employee benefits, and we also have P&C. So, being a multi-line insurer, we have the opportunity of having one approach and cross-selling across the business lines, which is a fantastic opportunity. We can only do that through technology.”

Over the course of her career, Yi Mien has been a champion of the transformative effect of technology in becoming a greater enabler for healthcare and healthcare insurance providers around the world. One area in particular that is close to her heart is the mental health space. In Hong Kong, the waiting time to see a psychologist is close to two years and if patients were to seek private care, it is an expensive solution. “Look at a country like Hong Kong, or Australia, they are so vast that there just aren’t enough practitioners to cover the breadth of the geography. Digital is the solution,” she says. “Digital enables people to seek, support and care at the time that is most convenient for them.”

“In the past two to three years, there has been a proliferation of digital tools. Recent studies have shown that digital tools are as good as, if not better, than in-person therapy because customers prefer to talk to a robot rather than face-to-face because they feel that the robot is not judging them.”

Another example that Yi Mien highlights is in the UK, where a VR program has been developed by programmers that is therapy through gameification. The treatment is consistent every time and because of its mobile platform, it is accessible. “We can provide it where you work,” she says. “That’s just one example as to how we can destigmatise mental health through technology.”

AXA operates within a broad healthcare ecosystem, an ecosystem made up of partners, providers and doctors and Yi Mien stresses that in the future of insurance, it will be impossible for insurers to control the ecosystem. “I don’t foresee a future where that happens,” she says. “Partnerships are incredibly important. Things are moving so fast there’s no way we can catch up alone. We need to have partners, collaborators, who are working together to ensure we are at the top of our game and at the forefront of innovation.”

“Over the course of our lives, so many different things can happen and so people will need better care and support. By having a collection of data that represents our customer’s needs we are able to push or suggest services that better meet those needs. In order for us to do that, we need to have players collaborate in the ecosystem. It’s imperative.”

As AXA continues this digital growth journey, the next few years will be defined by improving the agility of the digital companion in order to improve the interaction with customers. AXA will also be looking at developing a digital marketplace in which customers can go shopping within an AXA owned digital platform. For Yi Mien, though, the future is clear for AXA and in order to be successful, she feels it’s down to one thing. “AXA has a clear digital strategy for sure, where it will transform its digital system and build new IT infrastructure to transform the customer experience,” she says. “But the technology is only one part of the story.”

“Unless we can transform the customer experience to deliver a service they truly value, then technology doesn’t do anything. It’s important to recognise that technology is enabling us to transform healthcare, to make it easier, faster, and cheaper for people to receive care. That means in the long-term, sustainable healthcare and health services, which fits into sustainable insurance.”