Most financial institutions cited the complexity of new technology systems as their top barrier to innovation, PYMNTS Intelligence found.

Nearly all institutions used machine learning and AI for fraud detection, while behavioral analytics and device fingerprinting reached universal adoption among surveyed institutions.

The share of institutions developing fraud systems in-house that still combine third-party technology with proprietary tools was 93%, underscoring how fraud defenses depend on multiple systems working together.

Banks have plenty of fraud technology, but getting all of it to work as a cohesive line of defense is becoming a problem.

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The PYMNTS Intelligence report “Scams Get Personal: Why Banks Pay More to Fight Fraud” found in September that 78% of financial institutions said the complexity of new technology systems was their top barrier to innovation, up from 48% a year earlier. It was the largest increase among the 14 barriers tracked in the report.

The finding came after years of adding tools designed to identify suspicious customers, devices and transactions. Behavioral analytics, device fingerprinting and enhanced knowledge-based authentication reached 100% adoption among surveyed institutions in 2026. Machine learning and artificial intelligence were used by 96%, as were rules-based algorithms.

Adding another detection capability, however, isn’t the same as improving the overall defense. The data showed that institutions were stacking overlapping systems, making the connections among those systems a growing operational issue.

Integration becomes more important as fraud becomes harder to isolate at a single checkpoint.

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