Soaring losses accelerate investments in anti-fraud tech

The pandemic has opened a Pandora’s box of global fraud. Faced with a seemingly endless onslaught of schemes and scams, anti-fraud pros have doubled down on analytics, finds the latest anti-fraud technology study by the Association of Certified Fraud Examiners (ACFE) and SAS.

Based on survey responses from nearly 900 ACFE members worldwide, the report illuminates how organizations across sectors are using technology to fight fraud. Notably:

• More than 40% of respondents reported accelerating their use of data analytics significantly (14%) or slightly (29%) amid the pandemic.
• 60% expect their anti-fraud tech budgets to grow over the next two years.
• Advanced analytics topped the investment list, particularly artificial intelligence (AI) and machine learning (cited by 26% of respondents) followed by predictive analytics/modeling (22%).

“Navigating the pandemic-forged business landscape, we’ve seen increased reports of fraud – a trend we don’t expect to slow any time soon,” said Bruce Dorris, J.D., CFE, CPA, President and CEO of the ACFE. “To stay ahead of not only the increased volume of fraud instances, but also emerging trends, it’s critical for organizations to be flexible and proactive in adopting new anti-fraud tools and processes.”

Anti-fraud analytics initiatives and technology

• Analytics is an indispensable fraud detection tool. When asked about their use of analytics, nearly all survey participants indicated their organization’s use of data analytics was beneficial in helping them boost the volume of transactions reviewed or suspected fraud cases identified (99%); the timeliness of their anomaly detection (98%); their efficiency in automating time-consuming tasks (98%); and their overall accuracy in reducing false-positive rates (97%).
• Data-sharing consortiums are gaining momentum. Internal structured data sources remain the crux of most organizations’ anti-fraud analytics initiatives (cited by 80% of respondents), but many are also tapping a variety of external data sources, including public records (41%), law enforcement or government watch lists (31%), social media (29%), other third-party data (25%), and data from connected devices (25%). Moreover, 34% of respondents reported they contribute to data-sharing consortiums – and another 24% indicated that, while they do not currently contribute, they would be willing to do so in the future.
• Organizations are using a variety of emerging technologies to fight fraud. The report highlights the growing use of technologies like physical and behavioral biometrics, computer vision analysis, robotic process automation (RPA), blockchain, and virtual and augmented reality. Current use of these technologies ranges from 7% (virtual/augmented reality) to 34% (physical biometrics) of surveyed organizations. Among respondents from organizations not using a particular emerging technology, 13% (virtual/augmented reality) to 19% (RPA) expect to deploy it within the next one to two years.

“Resoundingly, the anti-fraud professionals surveyed said analytic technologies are helping them identify more fraud more quickly and accurately,” said study co-author Andi McNeal, CFE, CPA, Director of Research at the ACFE. “Time-tested techniques like exception reporting and anomaly detection remain mainstays, yet growing tech budgets and the 150% leap in AI adoption anticipated over the next two years signal that organizations aren’t resting on their laurels. The landscape could look quite different when we resurvey in 2023.”

“Even as proliferating nline and digital channels have heightened risks and created lucrative new avenues for criminal exploitation, there’s a silver lining,” said Stu Bradley, Senior Vice President of Fraud and Security Intelligence at SAS. “These channels allow organizations to capture droves of data that can be integrated to better monitor trends and, ultimately, take a more preventive stance against rapidly shifting threats.”

Source: https://www.helpnetsecurity.com/2022/02/15/anti-fraud-analytics-doubled/