The use of artificial intelligence (AI) and machine learning in anti-fraud programs is expected to nearly triple by the year 2026, with 83% of organizations expected to implement generative AI as part of their anti-fraud programs, according to the 2024 Anti-Fraud Technology Benchmarking Report by the Association of Certified Fraud Examiners (ACFE) and SAS.
The report surveyed organizations across many industries, with insurance making up 5% of respondents, or the fourth-most of any industry. The top three industry respondents were banking and financial services (22%), government and public administration (22%), and professional services (13%).
The report states that “a majority of organizations (61%) either currently contribute or are willing to contribute to data consortiums to aid in their anti-fraud efforts,” and that “[t]hree in five organizations (59%) expect to increase their budgets for anti-fraud technology over the next two years.” The top concern with implementing such technology is budget or financial restriction for 82% of organizations, the report adds, however, that “more than 50% of anti-fraud programs currently use or expect to adopt computer vision analysis, robotics, and behavioral biometrics at some point in the future.”
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These statistics represent a “growing momentum around these tools,” since the prior report in 2022, in which “26% of organizations expected to adopt this technology over the next two years, while 32% of organizations in [the] current study [plan] to adopt implement AI and machine learning in the near future.”
Current AI Usage
The report states that, currently, two in five organizations (40%) use physical biometrics as part of their anti-fraud program, and another 17% expect to adopt this technology in the next two years. Furthermore, “the use of both biometrics and robotics in anti-fraud programs has steadily increased over the past few years,” with biometrics seeing a 14% increase between 2019 and 2024 and robotics seeing an 11% increase within the same timeframe.
According to the report, “More than 90% of organizations in our study use some form of data analysis as part of their anti-fraud program…the most common uses of fraud analytics are exception reporting and anomaly detection (57% of organizations) and automated red flags and business rules monitoring (54% of organizations).” Every technique the survey asked about is expected to be adopted by more organizations in the next year or two, the report states, with AI and machine learning having the largest anticipated adoption rate, with nearly one-third of organizations that do not currently use it expecting to add it to their anti-fraud programs in the near future.
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The report notes, however, that that, “despite the expected increase in the use of every data analysis technique in our study, reported adoption rates have shown little growth since 2019, highlighting the slow pace at which organizations are able to implement new technologies.”
Analyzing the Results
“Fraud detection has become more complicated with the exponential growth of the amount of data that all organizations maintain. Even the most skilled fraud professionals now lack the ability to adequately analyze these growing data sets for warning signs of fraud without some form of machine assistance,” notes CLM member Ian Stewart, attorney at law, Wilson Elser Moskowitz Edelman & Dicker LLP. “Though 90% of organizations currently use some form of data analysis as part of their anti-fraud programs, more sophisticated predictive analytics and modeling for anomaly detection and automated red flags will continue to be adopted across industries at a growing rate.
“These new AI-driven data analytics tools will help fraud professionals mitigate losses from fraudulent disbursements, expenses, financial theft, money laundering and data privacy/cybersecurity risks. Although it is doubtful that machines will ever fully replace human judgment or the subjective due diligence needed for effective anti-fraud analysis, anti-fraud programs without machine assistance are largely a thing of the past.”