DataVisor, an AI-powered fraud and risk management platform, released its 2026 Fraud & AML Executive Report, revealing an AI Readiness Gap between rising concern over AI-driven fraud and financial institutions’ ability to defend against it.
The report found that 74% of surveyed senior fraud and AML leaders across banks, credit unions, fintechs, and digital payments platforms cite AI-driven fraud as a top threat, yet 67% say their organizations lack the infrastructure to deploy effective AI defenses. DataVisor defines this growing disconnect as the ‘AI Readiness Gap’—where attackers are outpacing institutional capabilities—and helps close it with secure LLM-based AI agents that boost both fraud detection and day-to-day operational efficiency.
As generative AI enables increasingly sophisticated threats—including deepfakes, synthetic identities, coordinated fraud rings, and automated scam campaigns—many institutions remain constrained by fragmented data, legacy detection models, and outdated governance not designed for today’s threat environment.
“Financial institutions are facing attackers that operate at machine speed, but many defenses still operate at legacy operational speed,” said Yinglian Xie, CEO and co-founder of DataVisor. “Closing the AI Readiness Gap requires modern foundations—unified data, adaptive machine learning, adoption of LLM-based AI agents, and operational models designed for continuous, real-time response. The organizations that modernize their infrastructure and workflows will be best positioned to stay ahead.”
The research identifies legacy infrastructure, organizational silos, and outdated operating models as the primary friction points slowing institutions’ ability to respond to rapidly-evolving fraud and financial crime threats. At the same time, financial institutions are actively working to close these gaps. Roughly 81% of organizations are considering or implementing a unified approach across fraud and AML, and 74% say that achieving a single, comprehensive view of risk would materially improve detection effectiveness. These findings reflect growing recognition that unified intelligence and integrated workflows are essential to countering increasingly coordinated and adaptive attacks.
In addition, the report reveals a shift in how executives view AI’s role in fraud and AML operations. While AI has traditionally been associated with detection and scoring, leaders now recognize that improving investigation workflows and operational decision-making can have the greatest impact. Half of executives rank investigator assistance as the top AI use case, surpassing detection and scoring (44%), reflecting a growing priority on accelerating investigations, improving decision speed, and empowering teams to act on risk earlier.
To actively help institutions close the AI Readiness Gap and modernize operations, DataVisor provides AI capabilities across all end-to-end workflows: detection, optimization, investigation, and reporting. Financial institutions start benefiting from AI by detecting complex rings and emerging fraud patterns—achieving a 50% detection lift while reducing false positives.
Furthermore, LLM-based AI agents can optimize and tune rules, reducing false positives by 42%, and generate AI-assisted alert reviews and SAR narratives to dramatically decrease review time by up to 60% and increase SAR filing efficiency by up to 90%.
The report also examines how real-time payments, faster digital onboarding, and increasingly diversified customer interactions are reducing the time to detect and stop fraud, placing greater pressure on institutions to modernize their fraud and AML operating models. It provides expert guidance on how organizations can strengthen their data foundations, unify fraud and AML operations, and operationalize AI to improve detection, investigation efficiency, and overall risk management.
