The Green Sheet Online Edition
January 12, 2026 • 26:01:01
From insight to trust: The next stage of data monetization in banking
Banks are sitting on an enormous volume of transaction data. Used carefully, that information can become the foundation of new products, pricing models and relationship-driven offers. Used irresponsibly, and it's a compliance nightmare.
Research from McKinsey & Co. shows that payments providers analyzing consumer and merchant behavior can generate new income streams by transforming insights into targeted services rather than relying purely on transaction fees (see tinyurl.com/3h5akcke).
The difference between insight and impact lies in execution. Real-time data enables a bank to identify when a customer's behavior signals a potential need. A frequent traveler, for example, could be offered a multi-currency wallet at the exact moment they book a flight, while a regular fitness subscriber might be introduced to a savings plan linked to health goals.
Reward programs that integrate across a customer's entire portfolio consistently outperform those limited to individual products. Analysis by EY (see tinyurl.com/4b3x43fe) found that cross-product loyalty models drive higher retention and wallet share, particularly when combined with personalized data analytics.
Yet many banks still struggle to act quickly enough on insights. The Financial Brand (see tinyurl.com/mr2enhy9) noted that many institutions are "drowning in customer data but can't act on insights fast enough."
Responsible monetization means embedding insights directly into the customer journey, supported by strong data governance and transparency. Banks must view data not as a commodity to exploit but as a relationship asset that, when managed well, can improve both customer experience and profitability.
Innovation within compliance
The line between innovation and regulation in banking is narrow but navigable. The General Data Protection Regulation (GDPR) and PSD2 have redefined how institutions collect, share, and process data, demanding that all experimentation be transparent, consensual, and auditable.
The European Banking Authority (see tinyurl.com/5e3d2ahh) describes open banking as a framework built on availability, accessibility, and analytics, but only if those principles coexist with security and privacy.
One effective solution is the self-hosted AI framework. Rather than relying on external cloud environments, banks can develop and test models within their own infrastructure, keeping sensitive data under direct control. This model allows internal teams to innovate rapidly while maintaining compliance and auditability.
Retail Banker International (see tinyurl.com/34anjhpp) observed that institutions adopting self-hosted systems can experiment more freely without increasing regulatory risk.
Practical implementation means building modular AI platforms that operate behind secure firewalls, where consent management, version control and logging are native functions. When innovation and compliance teams collaborate from the outset, product launches occur faster and with fewer downstream legal complications.
Explainable AI and customer trust
As artificial intelligence becomes integral to credit scoring, fraud prevention and marketing, the question is no longer whether to use it but how to make it accountable. Customers and regulators alike want to know why a model has reached a particular decision. The concept of explainable AI, or xAI, is therefore moving from theory to requirement.
A study published on arXiv (see tinyurl.com/4xhtku8b) outlined the twin expectations facing financial institutions: technical transparency and governance transparency. It is not enough for algorithms to work accurately; they must also be interpretable, reproducible, and free from hidden bias.
Explainability can take several forms. Customers need clear reasons when they are declined for a product or flagged for review, while regulators require detailed logs of data sources, model weights, and training methods. Building those layers of explanation into system design strengthens both oversight and customer confidence.
A transparent AI model is easier to audit, easier to correct, and far more likely to be trusted by the people it serves.
Learning from market leaders
Some banks have already demonstrated how AI can create growth rather than just reduce costs. Business Insider (see tinyurl.com/2wck27ud) reported that JPMorgan Chase, Goldman Sachs and Citi are investing heavily in generative and predictive AI tools that support both client-facing services and back-office productivity.
These investments are not simply about automation; they are aimed at new sources of revenue through improved decision-making and customer engagement.
Banking Exchange (see tinyurl.com/3mx4ewfy) recently noted that JPMorgan achieved leading status in "Responsible AI" implementation, citing around $2 billion in measurable returns and multiple patents focused on explainable models.
The common thread among these frontrunners is alignment between data strategy and commercial outcomes. They link AI projects, not just to cost efficiency metrics, but also directly to business goals such as cross-selling, product uptake.
This distinction defines leadership in the AI era. Growth-oriented banks view data as an asset to expand relationships, not simply a resource to automate processes. They treat compliance as an operational advantage that enables innovation within boundaries, rather than a constraint to be managed after the fact.
The road ahead
Payments data sits at the intersection of customer behavior and financial performance. Banks that use it responsibly, experiment securely and make their AI transparent will be the ones that set the standard for the next decade. Monetization, compliance and explainability are not competing priorities; they are complementary disciplines that, together, make digital banking sustainable.
It is clear that the technology and regulatory frameworks are ready. What remains is commitment. The institutions that integrate responsible data use, ethical AI and transparent governance into their strategy will not just comply—they will lead.
Radi El Haj, RS2's CEO and executive director, has been in the payment industry for more than 25 years. Radi specializes in the areas of issuing, acquiring, clearing and settlement, ecommerce, and accounting. Colleagues and clients benefit from his international experience, global network and experience with the technical and product development units. Radi was appointed chief executive officer of RS2 in January 2013. 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. For more information about RS2, please visit www.RS2.com; contact Radi El Haj via LinkedIn at https://www.linkedin.com/in/radie.
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