AI for fraud detection to triple by 2021

The inaugural Anti-Fraud Technology Benchmarking Report examines data provided by more than 1,000 ACFE members about their employer organisations’ use of technology to fight fraud. Other notable trends include:

• The rise of biometrics. About one in four organisations (26 per cent) use biometrics as part of their anti-fraud programmes; another 16 per cent foresee deploying biometrics by 2021.
• Increasing budgets. More than half of organisations (55 per cent) plan to increase their anti-fraud tech budgets over the next two years.
• Changing data analysis techniques. By 2021, nearly three-quarters of organisations (72 per cent) are projected to use automated monitoring, exception reporting and anomaly detection. Similarly, about half of organisations anticipate employing predictive analytics/modelling (52 per cent; up from 30 per cent) and data visualisation (47 per cent; currently 35 per cent).

“As criminals find new ways to exploit technology to commit schemes and target victims, anti-fraud professionals must likewise adopt more advanced technologies to stop them,” said Bruce Dorris, JD, CFE, CPA, President and CEO of the ACFE. “But which technologies are most effective in helping organisations manage rising fraud risks? The answer to this question can be crucial in successfully implementing new anti-fraud technologies.”

Dive deeper online: Trends by industry and more

Complementing the benchmarking report, SAS’ online data visualisation tool allows users to analyse survey data by industry, geographic region and company size. Survey respondents hail from 24 industries – most prevalently banking/financial services (21 per cent) and government/public administration (17 per cent) – and span the globe. The size of their employer organisations ranges from less than 100 employees to more than 10,000.

“The tools available for fraud prevention are now more intelligent than ever. We’re no longer restricted to merely reacting to fraud after it happens – with the right AI-enabled tools in place, anti-fraud teams can now begin to intelligently predict potential danger spots and flag up early warning signs to ensure efforts are co-ordinated and effective,” said Laurent Colombant, Continuous Controls and Fraud Manager at SAS. “The emergence of AI, machine learning and predictive modelling is helping investigators to pre-emptively detect fraudulent activity, allowing them to stay ahead of the increasingly sophisticated techniques being employed by criminals.”