Study: AI Fails to Shrink Corporate Fraud Teams

- According to the SEON report, 94% of fraud and compliance leaders plan to add at least one full-time hire in 2026, an increase from 88% in the previous year. Additionally, 83% of these leaders expect their budgets for fraud and anti-money laundering (AML) to rise. - The problem is compounded by fragmented data and systems; while 95% of organizations report some level of integration between their fraud and AML systems, only 47% have fully integrated workflows. This lack of a unified view makes it difficult for AI tools to be fully effective and for teams to work efficiently. - Criminals are increasingly using AI to perpetrate more sophisticated and scalable attacks, including deepfake technology, synthetic identity fraud, and advanced phishing campaigns. This has led to a significant rise in fraud, with one study noting a 1,210% increase in AI-driven attacks against its major U.S. customers. - The financial impact of corporate fraud is substantial and growing. In the U.S., companies reported losing an average of 9.8% of their revenue to fraud in the past year, a significant increase from the year prior. Globally, losses from online payment fraud are projected to reach $362 billion by 2028. - Despite the challenges, 98% of organizations are now using AI in their fraud and AML workflows, and 95% are confident in its ability to detect and prevent fraudulent activity. The primary application of this technology is for transaction monitoring. - The most common types of fraud that companies are facing include account takeovers, which account for nearly a third of all U.S. fraud losses, followed by scams/authorized fraud and synthetic identity fraud. - The implementation of new fraud prevention vendors is often a lengthy process, with 38% of companies reporting it takes one to three months to go live, and 24% taking four months or longer. These extended timelines can lead to increased costs and prolonged exposure to fraud.