AI is rapidly becoming a central tool in the fight against financial fraud, yet many institutions are not getting the full benefit from their investments.
Last year alone, fraud cost global financial institutions over $1tn, despite billions spent on prevention. While rules-based systems remain useful for catching known fraud patterns quickly, they are no longer enough. Criminals have learned to exploit the gaps, deliberately keeping transactions under detection thresholds, claims RegTech firm Hawk.
For example, fraudsters might use stolen credentials to make a series of peer-to-peer transfers just under the bank’s alert limit, such as multiple payments below $200, avoiding suspicion entirely. At the same time, overly strict rules can inconvenience legitimate customers, such as a night-shift worker whose online purchases keep being flagged simply because they happen outside regular business hours.
This is why context matters. Effective fraud detection requires understanding patterns across evolving threats without creating unnecessary friction for genuine users. AI brings this capability, using advanced models to identify anomalies, reduce false positives, and detect subtle behavioural deviations that rules alone cannot.
However, three major pitfalls frequently limit the success of AI fraud prevention strategies.
The first pitfall is failing to make full use of internal data. Many financial institutions believe they need costly third-party data sources or integrations to improve fraud detection. Yet, most already hold untapped transaction and customer data that, if combined, could power highly effective AI models. By linking customer profiles with transaction histories, AI can spot unusual activity, such as multiple accounts registered with similar email patterns, and flag it before losses occur.
The second pitfall is using generic, one-size-fits-all models. Pre-built AI solutions can detect common fraud types quickly but often lack the precision required for specific customer groups. A transaction flagged as suspicious for a retail client might be normal for a high-net-worth individual. Custom models, trained on an institution’s own data, reduce false positives and adapt to unique risk profiles, but they require time and investment—creating a trade-off between speed and accuracy.
The third pitfall is ignoring explainability. Many financial institutions rely on black-box AI systems that provide no insight into why transactions are flagged. This slows investigations, frustrates analysts, and creates compliance challenges as regulators increasingly demand transparency and consistency in algorithmic decision-making. Interpretable AI models, by contrast, explain alerts in clear terms, such as flagging multiple accounts with nearly identical names, enabling faster investigations and improved regulatory compliance.
As financial crime grows more sophisticated, avoiding these pitfalls will be essential for institutions looking to maximise AI’s potential in fraud prevention while maintaining customer trust and meeting regulatory expectations.
Copyright © 2025 RegTech Analyst
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