Artificial intelligence has become standard across fraud and anti-money laundering operations, but rising adoption is not yet simplifying the work for finance, risk and compliance teams.
According to SEON’s AI Reality Check: 2026 Fraud & AML Leaders Report, 98% of organisations already use AI in fraud or AML workflows.
The research surveyed 1,010 fraud, risk and compliance leaders across payments, fintech, financial services, retail, e-commerce, gaming and compliance organisations. The report shows that adoption is widespread, but operating complexity remains.
For CFOs, the most significant finding may be the continued rise in spending. Some 83% of organisations expect their fraud and AML budgets to increase in 2026, suggesting that AI is being treated as an expanding business capability rather than a short-term technology experiment.
However, the report also points to a return-on-investment challenge. While 95% of respondents report some integration between fraud and AML workflows, only 47% operate on fully integrated platforms.
As a result, organisations may be investing in multiple tools without achieving a complete view of risk, customer activity and investigative outcomes.
Integration limits the value of AI
The visibility gap is substantial. Eighty per cent of respondents say obtaining a unified view across fraud and AML systems is at least somewhat difficult, while more than 40% describe the challenge as extreme or very significant.
For finance and compliance leaders, fragmented workflows can mean duplicated reviews, inconsistent risk scoring and higher costs associated with false positives. They can also make it harder to measure whether an AI investment is reducing losses, improving alert quality, shortening investigation times or strengthening regulatory reporting.
The survey suggests that AI is augmenting rather than replacing specialist teams. More than 85% of leaders believe AI agents should operate as copilots for analysts, while only 12% expect AI eventually to replace them.
That distinction is reflected in hiring plans. Ninety-four per cent of respondents plan to add at least one full-time fraud or AML professional in 2026, up from 88% the previous year.
Human specialists are increasingly being redirected towards complex investigations, model oversight, regulatory reporting and wider risk strategy, while AI handles high-volume tasks such as transaction monitoring, anomaly detection and alert summarisation.
For CFOs, the message is clear: AI may increase capacity, but its financial value depends on integration, governance and measurable operational outcomes. Automation alone will not resolve fragmented data or remove the need for expert judgement.
