How Machine Learning Speeds Up Fraud Detection Posted on : Jun 08 - 2019

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible.

The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

For forensic accounting investigators, unearthing these crimes manually is an uphill climb. “The fundamental issue is that there is a flawed approach in examining fraud, since fraudsters know the rules that are set up to catch them,” says Justin Bass, chief data science officer at Crowe, the global accounting, consulting and technology firm combining specialized industry expertise with innovative technology solutions.

Bass provides the example of money laundering rules, which require banks to report any cash transactions of more than $10,000 to regulatory authorities. In response, “fraudsters simply break up the cash transactions into smaller amounts,” he explains. “The rules are created to catch these smaller amounts, but then the fraudsters circumvent them with other methods — which leads to creation of other rules and other subsequent actions by fraudsters to evade those new rules.

Machine Learning To The Rescue

Now there is a way to circumvent fraudsters via the use of machine learning (ML), the subset of artificial intelligence giving computers the ability to scan a haystack of data in search of the proverbial needle and progressively improve this capability through continuous learning.

Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements — instantly.

Applying an algorithm to this massive volume of data to tease out unique interrelationships presents a greater likelihood of detecting anomalies indicating fraud. “Whereas people generally can visualize three or four dimensions when evaluating the accuracy of a purchase order, machines can examine innumerable dimensions to ferret out the truly suspicious activities,” Bass explains.

To that end, Crowe has developed a proprietary ML tool called Crowe Data Anomaly Detection that has allowed the firm’s forensic accounting investigators to focus their efforts on higher-risk cases, reducing the time spent on those that don’t pan out, says Bass, whose team created the fraud-busting solution.

“We let the data tell us where to look, as opposed to us having to look everywhere,” says Tim Bryan, one such investigator and a partner in the Crowe forensic accounting and technology services group. View More