Report: AI Use Grows, But So Do Fraud Teams

- According to the SEON report, 94% of surveyed fraud and compliance leaders plan to increase their headcount in 2026, an increase from 88% in the previous year's survey. This is happening alongside 83% of these leaders expecting their budgets for fraud and AML to grow. - The primary driver for increased investment isn't a lack of faith in AI, as 95% of leaders are confident in its effectiveness. Instead, the issue is that criminals are also leveraging AI, which increases the complexity and volume of threats, requiring more human oversight. Deloitte forecasts that generative AI could contribute to U.S. fraud losses reaching $40 billion by 2027. - AI is primarily being used to augment, not replace, human teams; 85% of leaders see AI agents as support tools. AI excels at processing vast amounts of data in real-time to flag suspicious activities, which are then passed to human analysts for nuanced review and final decision-making. - A significant challenge hindering AI's full potential is data fragmentation; 80% of organizations find it difficult to get a unified view of their data because their fraud and AML systems are not fully integrated. This lack of a unified view is a major performance constraint, pushing companies to invest in integration. - The "black box" nature of some AI models presents a challenge for regulatory compliance. Regulators often require clear explanations for why a transaction is flagged, and if an AI system cannot provide a transparent rationale, it can create accountability and fairness concerns. - While AI helps reduce the rate of false positives compared to older rule-based systems, they are still a significant issue. High rates of false positives can damage the customer experience and reduce an institution's trust in its own AI tools. - Generative AI is a double-edged sword; while defenders use it to simulate fraud scenarios for better training of detection models, fraudsters use it to create more believable phishing messages, deepfake verification videos, and synthetic identities at scale. - The top use case for AI in this sector is for transaction monitoring, as cited by 30% of leaders in the SEON report. AI-driven systems analyze behavioral patterns and transaction history in real-time, which is more effective than relying on static, pre-defined rules that may not catch novel fraud tactics.