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?
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.
It’s no wonder then that financial institutions appear in their turn to be taking anti-MLA compliance seriously: 51.5 percent of respondents in a recent survey drawn from banks and insurers who work in risk, fraud, compliance and finance said that anti-MLA budgets would increase. But is this money well-spent? Judging from the HSBC example, maybe not so much.
According to the OCCRP, HSBC is the main culprit in the Laundromat case, having processed more than US$500m in cash through its British and foreign branches. Banks like HSBC claim that despite having sophisticated units dedicated to rooting out financial crime, the volume of payments — billions a year — makes such work difficult.
Others, like L Burke Files, an international financial investigator, call compliance checks at many western banks “desultory, and often little more than box ticking.” Files however also notes: “Most of the transactions I’m seeing here would have required substantial enhanced due diligence. It isn’t just individual transactions. It’s the repeated pattern.”
Repeated patterns and transaction volumes in the billions? This sounds like a job for ML. Sunil Mathew is the head of the Financial Crime and Compliance unit in Oracle Financial Services (OFS), and his job is to work with 9/10 major banks worldwide to help them comply with anti-MLA regulations. Part of that is looking into the applicability of ML in this domain.
OFS works with their clientèle to look at the banking products they have, the markets in which they operate and the regulations that apply in those markets to understand the risks they try to address. Then they map these risks to controls that need to be in place, and provide detection scenarios that implement these controls.
Mathew notes that in the last 15 years a set of commonly accepted scenarios has emerged for regulators around the world. One of those scenarios is monitoring rapid movement of funds as an indication that may point to MLA and generate alerts.
