Could financial fraud such as the Laundromat be avoided by applying machine learning to scan through data? And if yes, why is that not happening?

Predicting and acting upon financial fraud is one of the prime areas of application of advanced big data techniques like machine learning (ML). Earlier this week, a case of money laundering known as the Laundromat was uncovered by the Organized Crime and Corruption Reporting Project (OCCRP) involving a number of global banks active in the UK.

Could ML help prevent such incidents? What progress is there on this front, how does it fit in the bigger picture, what are the roadblocks, and what may be the repercussions of adoption?

There are many different types of fraud related to the financial industry. The Laundromat is a case of money laundering (MLA), which is estimated to generate about US$300 billion in illicit proceeds annually in the US alone.

While each type of financial fraud has its own characteristics and implications, MLA is considered important enough for the US to have its Department of the Treasury produce a National Money Laundering Risk Assessment (NMLRA) report in 2015.

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