Anti-Fraud Teams Struggling on AI, Tech

Key Takeaways

• 92% of anti‑fraud teams still lack AI tools
• Only 8% feel ready for AI‑enhanced fraud
• Data quality and governance cited as top barriers
• 75% worry about AI bias; only 18% test
• Budget and staffing constraints slow AI implementation

Summary

The Association of Certified Fraud Examiners’ 2026 Anti‑Fraud Technology Benchmarking Report surveyed over 700 anti‑fraud executives and found that 92% of teams still do not use AI, with only 8% feeling prepared for AI‑enhanced fraud attacks. Respondents cite data‑quality, governance, budget and staffing constraints as the biggest hurdles, while accuracy and bias concerns dominate AI‑specific worries. A parallel survey of 1,100 professionals shows a strong desire to increase both AI spend and human resources to keep pace with rapidly evolving fraud schemes. The findings paint a picture of high intent but low readiness across the industry.

Pulse Analysis

Fraudsters are leveraging generative AI to craft sophisticated schemes, and the ACFE’s latest benchmark shows most anti‑fraud units are still stuck in legacy processes. The report, based on responses from more than 700 global executives, reveals that a solid majority have not yet deployed AI, and merely eight percent consider themselves prepared for the surge of AI‑powered attacks. Core obstacles—poor data quality, fragmented governance, tight budgets and staffing shortages—are familiar challenges, but they become magnified when AI is introduced, creating a paradox where the technology meant to help is held back by foundational weaknesses.

Data governance emerges as the linchpin for any successful AI rollout. Over three‑quarters of respondents flagged bias and fairness as critical concerns, yet only 18% regularly test models for these issues. Without enterprise‑wide ownership of data quality and bias mitigation, AI tools can produce misleading alerts or miss fraud entirely. Moreover, regulatory uncertainty around explainability and privacy adds another layer of hesitation, prompting many firms to adopt a "wait‑and‑see" stance until clearer guidelines emerge. This environment forces risk leaders to balance the promise of AI against the practicalities of building robust data stewardship frameworks.

For organizations aiming to close the gap, the path forward lies in incremental investment coupled with strong data foundations. Prioritizing data cleansing, establishing clear governance roles, and piloting AI in low‑risk areas can demonstrate value while limiting exposure. Simultaneously, expanding human talent—both data engineers and fraud analysts—ensures that AI insights are interpreted correctly. By aligning technology upgrades with governance improvements, firms can transform AI from a speculative expense into a defensible, revenue‑protecting capability, staying ahead of fraudsters who are already exploiting the very same tools.