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In banking, artificial intelligence (AI) is often portrayed as an efficiency force-multiplier: automating back-office tasks, detecting fraud, reducing cost. Yet the bigger prize is less about cost and more about growth: unlocking new revenue streams through data monetisation, hyper-personalisation and dynamic pricing. At RS2, a platform that powers issuing and acquiring across banks and enterprises globally, we see how these possibilities can move from concept to profitable reality.

Unlocking Transactional Data for Revenue

Banks sit on rich transactional data – what customers buy, how they spend, when they engage. Historically, this data has helped reduce risk, fight money-laundering or optimise operations. But now it can be used to drive growth. According to an EY overview, AI-powered tools enable banks to personalise services, identify cross-sell opportunities and “potentially boost revenue streams.”

Consider a bank that analyses a customer’s payment behaviour, identifies recurring patterns (e.g., frequent travel, high hotel spend) and then offers a tailored premium travel card or concierge-style value add. Or a commercial bank that segments SMEs by payment volume and cash-flow profile and monetises by offering dynamic pricing on foreign exchange or supply-chain financing.

Responsible monetisation demands governance. A recent essay on monetising financial data with AI warns that “you’re sitting on a goldmine of data … but the major caveat is the need to manage risk”. The practical implication: invest in data-quality, maintain strict consent and usage controls, disaggregate personally identifying detail where possible and ensure transparency with customers. As banks move from “can we do this?” to “should we do this?”, the ones that succeed will embed data ethics, consent frameworks and explainability at the core.

Compliance and Innovation: Building Self-Hosted AI Frameworks

Growth-facing AI can’t sail past compliance. Banks need to remain within the bounds of regulatory regimes such as GDPR, PSD2 and CCPA. A key enabler is self-hosted or controlled AI infrastructure that allows experimentation without exposing sensitive data to third-party cloud vendors or uncontrolled derivative uses.

In the UK, the Bank of England notes that the future of AI in financial services demands both innovation and safety – building internal capabilities while contributing to systemic resilience. For banks this means: maintain internal model-hosting (or tightly controlled cloud with data isolation), build a “sandbox to production” pipeline where models are validated for bias, fairness and explainability, and treat regulatory engagement not as a blocker but as a design parameter.

With this architecture in place, banks can push beyond the cost-centre mindset (fraud detection, operations) into growth-mindset use-cases – real-time decisioning, dynamic pricing, micro-segment product design – all while retaining control over data flows, vendor risk and audit trails.

Explainable AI: Trust at the Front-Line

If AI is going to power new revenue models – dynamic offers, predictive cross-sell, hyper-personalised pricing – then customers and regulators alike must trust the outcomes. Enter explainable AI (XAI).

Explainability isn’t a nice add-on: it’s mandatory when AI touches decisioning that affects consumers (pricing, credit, product eligibility). If a customer is offered a differential rate based on their profile, they are entitled to know (in clear language) why. If a regulator challenges the fairness of an algorithmic decision, the bank must show the decision-tree, the bias mitigation steps and the audit trail of model monitoring.

As banks deploy AI in growth-facing scenarios, transparency becomes a strategic differentiator: one bank may claim to offer “smarter offers” – another will be able to document that those offers are fair, auditable and compliant. That traceability becomes a selling point when partnering with fintechs, regulators or corporate clients.

Lessons from Leading Banks: Growth-Not Just Cost-Cutting

While many banks still emphasise cost-cutting, the story is shifting. For instance, research from FIS shows that banks with a strong data strategy are tying AI investments to revenue outcomes, not just automation.

In practice, a global bank uses AI-driven cash-flow tools for corporate clients and is now preparing to monetise the service rather than treat it purely as a cost centre. Another major institution, NatWest, has embedded AI in its digital-assistant ecosystem and already reports improved customer engagement metrics and lower servicing costs.

From the experience at RS2, we see banks and FinTechs that pay attention to platform architecture, data lineage and flexible monetisation workflows succeed faster. The value flows not from a single “AI project” but from embedding AI into the payment rails, product lifecycle, pricing engine and loyalty ecosystem.

It is noteworthy that banks are not alone here: payments-technology providers like RS2 are collaborating with financial institutions to integrate AI into issuing and acquiring flows, offering a way to turn payments data into behavioural insight, and knowledge into value-added services.

Bringing it Together

For banks, the dominant mindset should shift from “AI as efficiency tool” to “AI as growth platform”. That transition requires three foundational capabilities: a clean, consent-driven data ecosystem; an AI infrastructure that balances innovation and control; and an organisational discipline around explainability, governance and monetisation strategy.

At RS2 we believe that the combination of payments technology, platform mindset and global scale gives us a front-row seat to this shift. The banks that lead in the next five years will be those that embed AI not in margins but in revenue lines – crafting new products, offering dynamic pricing, delivering real-time personalisation and monetising payments data in a responsible manner.

The future isn’t about AI simply making existing processes cheaper; it is about re-working how banks generate value. If your AI agenda stops at cost-cutting, you’re leaving the biggest opportunities on the table.

About RS2

RS2 is a leading global provider of payment technology solutions and processing services, offering a unified approach to managing payments across all channels for banks, integrated software vendors, payment facilitators, independent sales organizations, payment service providers, and businesses worldwide. RS2’s platform stands out as a robust cloud-native solution designed for both issuing and acquiring operations. With its advanced orchestration layer seamlessly integrating all aspects of business operations, clients gain access to comprehensive analytics, reporting tools, and reconciliation features. This empowers businesses to effortlessly expand their global footprint through a single integration, while also gaining valuable insights into payment processes and customer behavior, enhancing operational efficiency, increasing conversion rates, and driving profitability.

Learn more at RS2.com

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Artificial Intelligence in FinTech
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Do you build, buy or partner? When you need payment solutions it would seem that you only have three options. You can build a new system in-house, buy a solution outright or partner with a payments provider. All have advantages and disadvantages. Heres how AI can change that…

Building, rather obviously, requires having the capacity to build in-house. Few payments companies are going to need to develop world-class coding expertise in their IT departments. Buying is increasingly impossible – nearly everything works on a software-as-a-service model. Partnering is by far the most common approach to extending a company’s capacities. Working alongside an established provider of payments technology to integrate their solutions into your existing technology.

A staggering 70 cents in every dollar of a bank IT budget is spent on patching up old systems, and whether you build, buy or partner the aim is almost always to patch old systems rather than ‘rip and replace’. There is simply too much risk when completely overhauling legacy systems. So unless financial services companies are starting from scratch (like neobanks) then they will have a patchwork of modern and legacy systems gradually modernising over time.

But what if these aren’t the only ways to build new capacities and capabilities in payments? What if AI-enabled orchestration layers could offer a pragmatic, risk-mitigated and cost-effective fourth option? According to RS2’s latest research, this is not only possible, it’s already happening. And it’s driving measurable improvements in transaction success rates, fraud reduction and customer insights across global banking operations.

What is payment orchestration?

A payment isn’t a simple case of sending a fixed sum from one bank to another. There is a multi-part, often multi-national process to every payment that has to take place within fractions of a second, involving multiple companies and systems, some of them AI-based.

Just as each musician in an orchestra knows their individual part to play but needs a conductor to become a unified whole, a payment orchestrator makes sure each element in the payments chain works harmoniously. In practice, this means determining the optimal route for each transaction based on the payment itself: one particular payment might have more chance of being accepted going down one route than another, particularly when payments are being made across national borders. It means that merchants can connect with a single payment orchestrator and from there access an entire world of payments companies, each suitable for a certain part of certain payments. These transaction chains are also made to be compliant with regulations in whatever jurisdictions that they take place in.

One under-appreciated part of payment orchestration is the top-down view it gives over a merchant’s payments, and from there how it can be analysed to improve payments and the merchant’s operations as a whole. It can give merchants insight into payment trends, customer behavior, performance and fraud, and if these aspects of payments can be optimized then there is potential for significant cost savings.

This is key: the ultimate outcome of payment orchestration is reduced costs for merchants and their customers. Whether it is through reducing the cost of each payment through the most efficient processors or allowing data analysis to find ways in which to optimize payments, the ultimate outcome is always going to be cost savings.

Enter AI

Artificial intelligence has been a major news story for the past three years, but the real picture of what is happening and what could be happening in the space is much more complex and interesting.

Almost all of the press attention on artificial intelligence over the last years has been toward Large Language Models (LLMs) like ChatGPT. These can produce convincing bodies of text but this has little utility in payments beyond being a cheap alternative to customer-service agents. The real use of AI in payments has a longer history and is much more useful, especially when combined with the influx of data that can come from payment orchestration.

So, what can AI be used for in payments? Merchants and payments providers produce incredible amounts of data, much of which goes unanalyzed and sits inert in cloud storage, becoming a cost rather than a source of revenue. Machine-learning algorithms have shown an incredible ability to sort through this information and provide insights that no human could come up with. These insights can inform top-level decision-making (‘our customers are moving toward alternative payment methods’) or micro-scale adjustments (‘using payment service provider A instead of payment service provider B at weekends gives a 0.043% increase in acceptance rates’).

AI-enabled orchestration layers take this a step further. They connect all banking platforms—card management, UX, third-party services, ledgers, reconciliation, interchange, and more—into a central intelligence hub. The result is dynamic optimization of transaction routing, cost reduction in acquiring and FX, and a dramatic reduction in fraud and transaction failure.

The AI Orchestration Layer

Imagine that you have an orchestra with both veteran (perhaps even past their prime) musicians and enthusiastic newcomers. Hypothetically they can play the sheet music in front of them, but what they need is a conductor to bring it all together.

This is the AI orchestration layer. Instead of building, buying or partnering to upgrade individual services, an AI system can ensure that all of the existing parts of a company’s payments ecosystem are working as a unified, insight-driven whole.

With real-time fraud detection, transaction risk scoring, and automated escalation steps (like biometric authentication), AI orchestration layers significantly reduce chargebacks and improve compliance. Smart decline recovery techniques—such as real-time retries or alternative payment prompts—directly increase revenue and improve customer satisfaction.

AI also simplifies regulatory compliance. With built-in AML and KYC checks, suspicious activity monitoring, and auto-generated reporting, banks can meet growing compliance demands with fewer human resources and less manual intervention.

Beyond Build, Buy, or Partner

This isn’t just a new tool—it’s a new model. RS2’s white paper describes AI orchestration as the “fourth path” beyond build, buy or partner. Rather than risky system replacements, banks can phase in AI capabilities without ever compromising core operations. By implementing self-hosted AI within secure Virtual Private Clouds, RS2 ensures full control over sensitive financial data while delivering full interoperability with ISO 20022 messaging frameworks.

The result? Lower fraud, higher conversion rates, smarter compliance, and a customer experience that feels truly modern—all achieved without the disruption of traditional overhaul strategies.

Banks don’t need to choose between building from scratch, outsourcing, or stitching together third-party solutions. AI-enabled orchestration offers a more elegant, efficient, and secure way forward—and it’s available today.