Fraud examiners have a plethora of data analytics tools

Data analytics techniques play a crucial role in fraud prevention, detection and investigation. According to the 2020 Report to the Nations (ACFE.com/RTTN), proactive data monitoring and analysis are among the most effective anti-fraud controls. Organizations that undertake proactive data analysis techniques experience frauds that are 33% less costly and detect frauds 1.5 times as quickly as organizations that don’t monitor and analyze data for signs of fraud. And as stated in the 2022 ACFE Fraud Examiners Manual, “When properly used, data analysis processes and techniques are powerful resources for uncovering fraud. They can systematically identify red flags and perform predictive modeling, detecting a fraudulent situation long before many traditional fraud investigation techniques would be able to do so.” (See ACFE.com/FEM, Section 3: Investigation/Understanding the Need for Data Analysis.)

The sophistication and complexity of fraud schemes are growing and outclassing conventional fraud prevention, detection and investigation techniques. Fraudsters are developing new strategies to commit such crimes as vendor fraud, employee expense fraud, financial statement fraud, bribery and asset misappropriation.

Global data volumes continue to grow exponentially, but we can quickly harness this data to identify unusual patterns or red flags that may have previously gone undetected. In this column, we draw on ACFE resources to discuss ways that management and fraud investigators can use data analytics to prevent, detect and investigate fraud.

Fraud data analytics is the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to fraud. Fraud data analytics isn’t a new topic to readers of Fraud Magazine. Indeed, the ACFE, through the years, has published many reports and articles on the role of data analytics in our jobs. For example, the Anti-Fraud Technology Benchmarking Report (ACFE.com/techreport), which the ACFE developed in partnership with SAS in 2019, found that using data analytics techniques, such as data visualization, predictive analytics, artificial intelligence and machine learning, is expected to grow considerably.