Artificial Intelligence: As Fraudsters Get Better, So Must Your Defenses

In today's digital era, enterprises across industries are moving from paper-based to digital means of retaining records of their clients' transactions and the footprints of day-to-day operations.

Nevertheless, cases of fraud persist: methods of deception are evolving along with the times, continuing to result in financial or personal gains for fraudsters. Rising numbers of daily transactions increase the opportunities for deceit, making it harder and costlier to instate systems of prevention. In this context, the way an organization tackles fraud-related risks—how quickly and efficiently—determines its success against its competitors. In facing vast amounts of data, companies that can use this data to mitigate potential risks are also at an advantage.

So how do you properly anticipate fraud and mitigate its effects?

Artificial intelligence for fraud detection and prevention Artificial intelligence is a powerful option when it comes to searching for anomalies or suspicious transactions. However, of 750 fraudsters found and investigated between March 2013 and August 2015,1 only 3% were detected using proactive, fraud-focused cognitive analytics, whereas 44% were found by traditional whistle-blowers and other tip-offs.

This very low percentage for AI is concerning, given that the world of fraud is only getting more complex and costly. Indeed, a 2018 global survey of over 41,000 certified fraud examiners revealed that fraud accounted for $7 billion in losses. Small business lost almost twice as much as larger ones.

As long as trust issues in AI are carefully managed, data analytics can be a highly useful addition to any company's anti-fraud program: it can help limit potential financial and reputational damage from fraud and misconduct, while sending a message to would-be fraudsters that the risk of getting caught is high. Our team here at KPMG is deeply experienced in this area (we design and build fraud detection systems) and would like to outline five critical considerations for enterprises interested in detecting fraud using artificial intelligence:2

Quality of data: The data has to be accurate, up-to-date, consistent, and complete, while the sources of data need to be known and understood. The fraud tools deployed should fit the task at hand and be modeled on the processes that are relevant, such as the types of transactions or the involvement of particular functions. Separating fraudulent from "normal:" Given the vast amount of data...