Big Data versus money laundering: Machine learning, applications and regulation in finance
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?
“It isn’t just individual transactions. It’s the repeated pattern”
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.
The reason MLA carries this weight is clear even without reading the 100-page long document in its entirety. MLA has more than financial impact, as it is associated with activities ranging from trafficking people and drugs to terrorism and corruption. It’s no wonder then that governments around the world are trying to crack down on MLA by means of regulation on financial institutions.
Financial institutions have to comply with a set of rules imposed by regulators, and are audited to verify their compliance. If found in negligence of their duties, they are faced with legal consequences. For example, HSBC-US entered into a deferred prosecution agreement (DPA) in the US in 2012, for failing to adequately monitor more than US$670 billion in wire transfers and $9.4 billion in purchases of U.S. bank notes from HSBC Mexico.