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As the perpetrators of accounting fraud become ever more sophisticated in their techniques, fraud detection needs to step up its game. Thankfully, a group of researchers have devised a new machine learning 'detective' that is able to analyze not just fraud at a single firm, but predict likely fraud across whole supply chains and industries.

A paper describing the team's approach was published in the journal Big Data Mining and Analytics on August 28.

Financial statement fraud, or, more commonly, accounting fraud may be a less frequent form of corporate fraud, but it is by far the costliest crime in the world. Perhaps the most famous cases of white-collar crime can be considered accounting fraud, when an enterprise manipulates the figures on its financial statements or other valuation data in order to make it appear more profitable than it is.

The collapse of US energy firm Enron, the largest bankruptcy in US history, came from their cooking of the books in collusion with their accounting firm. In 2008, Lehman Brothers declared bankruptcy due to insolvency, having concealed approximately $50 billion in debt through balance sheet fraud. In the late 2010s, American investment advisor Bernie Madoff managed to cheat clients out of a whopping $65 billion.

It is not only investors who are hurt by financial statement fraud. Hundreds of thousands of jobs can be lost, communities devastated, and, in the most extreme cases, through knock-on effects, it can threaten the stability of national economies.

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