AI Resilience Report forFraud Examiners, Investigators and Analysts

If you're thinking about a future career fighting fraud, here's some good news: AI is mostly being used to help fraud examiners — not replace them. According to the ACFE and SAS 2026 Anti-Fraud Technology Benchmarking Report, one-quarter of organizations (exactly 25%) now use AI/ML in their anti-fraud programs, up from 18% in 2024, and another 28% expect to adopt it by 2028. The tools target the most repetitive parts of the job — building case logs, summarizing transactions, and drafting reports.

A Fraud Magazine column from the Association of Certified Fraud Examiners explains that the industry is moving past predictive AI and GenAI copilots into the age of agentic AI — autonomous systems that don't just predict actions but adapt, decide and execute end-to-end workflows, with multi-agent AI workforces supporting KYC reviews, AML and fraud investigations expected to gain significant traction, augmenting analyst capacity and improving investigative consistency. But humans stay in charge: regulators demand a "reasoning chain" for every decision to ensure transparency and human-in-the-loop accountability ^[1]. That's why field surveillance, coordination with police and lawyers, and advising businesses — the messy, judgment-heavy work — remain firmly human.

In fact, demand is growing: the U.S. Bureau of Labor Statistics projects employment of financial examiners to grow 19 percent from 2024 to 2034, much faster than the average for all occupations ^[2], partly because AI-powered fraud is creating more cases to investigate, not fewer.

Adoption is moving fast in some areas and slowly in others. On the "fast" side, fraudsters themselves are forcing the issue: every AI-powered fraud modality examined has risen over the past two years, with deepfake social engineering seeing the sharpest surge — 77% of respondents reported a slight-to-significant increase, and a separate survey found that the vast majority of anti-fraud professionals want to increase both their investments in AI and their human staffing ^[3] because firms can't keep pace with AI-driven scams. Regulators are encouraging this too — a Notice of Proposed Rulemaking from FinCEN explicitly recognizes that fostering technological innovation, including generative AI and machine learning, is vital to improving financial crime compliance ^[4], which industry experts read as a directive to modernize.

But there are real brakes. Nearly nine in 10 (86%) organizations rate accuracy of results as important or very important in adopting GenAI, yet less than one in five (18%) say their organization tests AI models for bias or fairness, and only 6% feel completely confident explaining how their AI/ML models make anti-fraud decisions. Budget limits, poor data quality, and unclear rules also slow things down — tight budgets, poor data quality, staffing issues, and data governance are the biggest obstacles.

The takeaway for students: the future fraud examiner will work alongside AI agents, but skills in ethics, communication, investigation, and human judgment will be more valuable than ever.