The truth about nank fraud typologies: why they’re failing and what attackers exploit

Fraud teams are still working inside boxes that no longer fit the problem. Every meeting, every report, every vendor pitch starts with the same question: What type of fraud do you protect against?

The answer is usually a list: Authorised Push Payment (APP), Account Takeover (ATO), Card-not-Present (CNP). These labels help auditors, regulators, and analysts compare cases. They don’t help banks stop fraud. Typologies were built to describe incidents after they happened, not to detect them in real-time.

In 2025, that distinction matters more than ever. Fraud campaigns no longer stay within a single definition. They move freely across digital channels, employing cyber techniques, social engineering, and session manipulation in sequence. The result: typology thinking is now a bottleneck, not a framework.

In this article, we explain why talking about fraud typologies doesn’t help anymore in preventing banking fraud efficiently.

Attackers don’t care what we call them. They combine whatever tools will work - phishing, remote access, credential theft, social engineering - and adjust as soon as one barrier slows them down.

A single campaign might start with an SMS lure, shift into a spoofed call, install remote-access software, and finish with an “authorised” payment on the web. To the victim, it’s one continuous attack. To the bank, it becomes four separate cases, each filed under a different code.

The result is duplication, delay, and missed connections. Typologies break down complex attacks into fragments, and in doing so, they obscure the full picture.

Fraud, cyber, AML, and risk teams often operate as if they’re guarding different doors. Each uses its own tools, processes, and language. Typologies reinforce that divide by assigning each department its own set of categories.

Attackers don’t play by those boundaries. They move between them. They exploit the space where no single team owns the full attack chain. One major bank found that 90% of digital fraud began with credential compromise, technically a cyber issue that led to a fraudulent outcome. Both teams were involved. Neither saw the whole thing.

The problem isn’t a lack of technology; it’s that the structure of defence mirrors the structure of reporting. Typologies built for compliance have become the blueprint for response.

Typologies still matter for regulators and auditors. They keep reporting consistently and let risk teams track trends over time. But they don’t belong in detection logic or operational design.

When detection systems are built around categories, they look for symptoms instead of causes.
In a world of instant payments, that’s too late. By the time a transaction looks suspicious, the social engineering and device compromise have already done their job.

Typologies are useful for explaininglosses, not preventing them.

The industry needs a different lens, one that focuses on how an attack unfolds, not what label it fits. Attack Pattern Recognition (APR) replaces typology-based thinking with continuous, correlated detection.

APR works by connecting signals across devices, networks, applications, behaviours, and transactions. It reconstructs how an attack begins, evolves, and concludes. That correlation enables fraud and cyber teams to view the same campaign as a single event, regardless of which channel it impacted.

When systems share context, they stop guessing at labels and start recognising cause and effect. That’s how institutions move from reacting to reporting categories to preventing the attack itself.

Fraud prevention needs to evolve from a typology-centric approach to a threat-centric one. Instead of asking, “Is this APP or ATO?”, teams should ask, “Where did this start, and what signals confirm it’s still active?”

Attack Pattern Recognition (APR) enables that view. By rebuilding the full attack journey across users, sessions, and devices, it exposes the tactics and techniques behind every case. It shows not just what happened, but how and why it happened, the foundation of true prevention.

That shift doesn’t remove the need for typologies; it simply puts them where they belong: at the end of the process, not the start.

Typologies will remain part of how banks report fraud, but they can’t define how fraud is fought.
The institutions that move first will be those that stop building defences around categories and start building them around campaigns.

Fraud has outgrown the boxes we built to contain it. The next generation of defence will connect what’s happening across cyber, fraud, and AML in real time, guided by patterns rather than labels.

That’s how prevention becomes predictive and how banks finally see fraud the way attackers do.