Personalization Has Two Sides: Better Experiences And Better Fraud Defense
Jess Turner, Executive Vice President, Global Head of Open Finance & Developer Experience at Mastercard.
gettyWhen financial institutions talk about personalization, the conversation almost always starts, and often ends, with the more visible parts of the consumer experience: better recommendations, smarter offers, a more intuitive app.
These are important, but they don’t tell the full story.
The same data infrastructure that powers a tailored product recommendation can also detect a fraudulent transaction. The same behavioral signals that reveal a consumer is an avid traveler can also flag when their account suddenly exhibits spending patterns that look nothing like them.
Personalization isn’t just about engagement—it’s about security, and institutions that treat them separately are leaving value on the table.
At its core, personalization is about understanding the consumer. This means having insights into a consumer’s financial behavior: how they earn, spend, save and move money.
Yet the information needed to understand that financial journey is often fragmented across institutions, accounts and products, where the average consumer has five to seven financial accounts, according to research from American Banker with insights provided by Mastercard, where I lead open finance and developer experience. This leaves financial providers with blind spots, as they lack a complete view of each customer.
Open finance has made it possible to close that visibility gap at a scale that didn’t exist even a few years ago, by connecting permissioned financial data across accounts, payment rails and financial relationships, and then turning it into intelligent insights institutions can use, while giving consumers greater visibility and control over how their financial information is shared.
That enriched, contextualized data—spending velocity, merchant patterns, recurring payments, income flows—is precisely what fraud models need, too. Traditional fraud systems relied heavily on static identity attributes: a Social Security number, a date of birth, a billing address.
As bad actors have become more sophisticated by using AI to manufacture synthetic identities and automate account takeovers, static signals are increasingly unreliable. This is particularly challenging during digital account opening, when a synthetic identity may appear to be a legitimate applicant with a thin credit history.
One in ten financial institutions experienced more than 10,000 fraud attempts in 2024 alone, according to Alloy, spanning synthetic identities, account takeovers and credential theft. As these threats evolve, institutions need to look beyond traditional verification methods and consider a broader range of signals when assessing identity and risk.
What’s harder to fake is a complete financial life: the cadence of a weekly grocery run, the pattern of a direct deposit every other Friday or the way someone pays their rent, tops off their transit card and sends money to a family member. These behavioral fingerprints are extraordinarily difficult for a fraudster to replicate, and they’re the exact same signals institutions use to personalize offers and deepen engagement.
This convergence matters because most institutions are resource-constrained.
The American Banker research cited above shows that only 8% of banks and credit unions have the data maturity to support personalization at scale. At the same time, according to KPMG, 89% of banking executives are increasing their budgets to address cyber risks. These investments are often made in parallel—separate teams, separate budgets, separate technology stacks—when the underlying data problem is largely the same.
Institutions that recognize this can build once and benefit twice. A data enrichment capability that standardizes raw transaction streams into clean merchant labels, categories and behavioral indicators doesn’t just power a personalized savings recommendation—it also makes anomalous behavior easier to spot.
A consumer whose spending patterns suddenly shift from routine grocery purchases to high-value electronics at unfamiliar merchants looks different against a well-understood behavioral baseline. Without that baseline, the same transaction might not raise a fraud flag at all.
The business case reinforces this dual logic. According to Mastercard research published in partnership with FT Longitude, 60% of organizations adopting open finance have already developed the ability to generate real-time AI insights about risk and consumer engagement—not one or the other, but both from the same data foundation. And 57% have used those same capabilities to strengthen their security and fraud protocols.
Of course, using the same data for personalization and fraud defense raises the stakes on responsible governance.
Consumers are increasingly willing to share their financial data, but that willingness is conditional. Nearly half say it depends on understanding how their data will be used and what they’ll get in return. If institutions leverage behavioral data to improve someone’s banking experience and protect them from fraud, that’s a value exchange most consumers can embrace and that sets a stronger foundation of ongoing trust with their provider. If the governance isn’t clear, the trust erodes quickly.
Executives should look to embed security by design in all data projects—not as an afterthought, but as a foundational principle. Responsible data governance isn’t a constraint on innovation; it’s the precondition for it.
The institutions that get this right will be the ones that earn ongoing consent, which fuels better data, which drives both better personalized experiences and stronger defenses. It’s a flywheel in which consumers recognize the value and remain in control, while providers use increased data sharing to drive continuous innovation loops, with trust at the heart of what keeps it turning.
The financial services industry tends to silo innovation. Customer experience teams pursue personalization; risk teams pursue fraud prevention; data teams try to serve both and end up building twice.
The more productive frame is to recognize that personalization and fraud defense are two expressions of the same underlying capability: knowing your consumer well enough to serve them—and protect them—in real time.
That is the broader opportunity of open finance, and institutions that build their data foundations with both outcomes in mind won’t just deliver better experiences or catch more fraud. They’ll do both more efficiently, and while earning greater trust and building long-lasting relationships with the people they serve.
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