Artificial intelligence can help to reduce money laundering

7 years ago - Published in Finance, Technical Analysis

Predicting and acting upon financial fraud is one of the prime areas of application of advanced big data techniques like machine learning (ML). 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.

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.

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. 

Over the last several years, advancements in data science, including artificial intelligence (AI), machine learning and big data management, promise to stifle money laundering making the accurate evaluation of all transactions a viable reality. 

The technology can improve "acceptable costs." Financial institutions and issuers set dollar, volume and velocity thresholds for transaction monitoring. Above these thresholds, they attempt to screen all transactions for money laundering. Below them, they accept that some money laundering, terrorist financing, and other financial crimes might go unaddressed.

This Above the Line, Below the Line (ATLBTL) practice functions on a risk-based transaction monitoring process by which not all transactions are screened for possible money laundering and other financial crimes concerns.

The percentage allowed to pass unscreened is determined by institutions and federal regulators that work with them to ensure that they comply with AML regulatory guidelines. Industry estimates state these allowances run between three and 10 percent of all “below the line” (BTL) transactions. Institutions and regulators conduct periodic assessments to ascertain if the ATLBTL thresholds are functioning within their established risk tolerances.

Given technological advancements, ATLBTL is one example of the many compromises financial crimes teams no longer should have to make. AI for AML is built for the purpose of complementing transaction monitoring systems, increasing investigative efficiency, driving out false positives, and catching the false negatives that embody financial and reputational risk. With trillions in laundered money still going through banking systems, AI offers the opportunity to move toward a new standard.