INFORM has published findings from a survey of 88 financial crime professionals, highlighting AI adoption trends and growing interest in behavioural biometrics.
INFORM, a Germany-based developer of AI-powered decision-making software for risk, fraud, and compliance, has published results from its 'Artificial Intelligence & Anti-FinCrime Market Research Survey', drawing on responses from 88 financial crime professionals across large financial institutions.
The survey finds that AI has moved from an exploratory technology to a foundational element of financial crime defence strategies, with the majority of respondents already deploying AI across core compliance and detection functions.
Adoption patterns and primary use cases
According to the official press release, more than half of survey respondents (56%) represent banks, credit unions, and fintechs, and 50% work at organisations with more than 1.000 employees. Over half hold executive or middle management roles, lending institutional weight to the findings.
Current AI deployment is concentrated in high-volume, continuous monitoring tasks. Transaction monitoring leads adoption at 82.5%, followed by AML at 71.25%, and anomaly detection at 61.25%. Identity verification (37.5%) and customer behaviour analysis (33.75%) trail behind, suggesting that real-time monitoring remains the primary use case for AI in financial crime management.
When asked about the drivers behind AI adoption, faster detection emerged as the dominant motivation at 80%, followed by reduced false positives (72.86%) and improved accuracy (61.43%). Cost reduction was cited by 30% of respondents, and enhanced customer experience by 20%, indicating that operational performance rather than commercial objectives is the core rationale.
Behavioural biometrics as a complementary layer
A notable finding concerns the role of behavioural biometrics alongside AI. Some 56.25% of respondents identified it as the most valuable supporting tool in their anti-financial crime toolkit. The technology monitors subtle deviations in a user's established interaction patterns (including typing rhythm, mouse movement, and device handling) to identify account-takeover attempts, bot activity, and social engineering-driven transactions before a fraudulent event occurs.
Interest in expanding biometric capabilities over the next three to five years scored an average of six out of ten for organisational intent, signalling that adoption is expected to grow but has not yet reached maturity.
Separately, 56.79% of professionals said AI is most effective at pattern recognition in large datasets, pointing to the core analytical capability that institutions expect the technology to deliver.
Platform positioning and product context
The survey was conducted and published by INFORM, which also markets RiskShield, a financial crime management platform built on what the company describes as a hybrid AI model, combining rule-based logic with machine learning. The platform processes transactions in real time and integrates fraud prevention, AML compliance, and risk management in a single environment.
As financial institutions face mounting regulatory requirements and increasingly complex fraud typologies, the findings suggest that AI deployment strategies are maturing, moving from single-function implementations towards integrated, multi-layered defence architectures that combine machine learning with behavioural analytics.
