Money laundering, fraud and other types of financial crime are an entrenched fixture of the global banking system. In fact, the United Nations Office on Drugs and Crime and the Financial Action Task Force estimates that between two and five percent of global GDP — between $800 billion USD and $2 trillion USD — is laundered on an annual basis.
According to Richard Stocks, Managing Director of Financial Crimes and Compliance Solutions at Pitney Bowes, financial institutions are fighting back against these crimes with sophisticated technology.
“In the last five years global AML spend has risen from $5.9 billion to over $8.2 billion,” said Stocks. However, he also noted that 25 percent of financial institutions still do not conduct any AML and anti-fraud activities across their global footprint. “Banks and other financial institutions have invested in AML and anti-fraud solutions but have not been able to stem criminal activities, or able to achieve a return on their investments in this technology. A significant part of the problem, here, is that frequently, financial institutions are starting out with poor quality data, which makes the job of identifying an event very difficult,” said Stocks.
Once an alertable event is created it is down to people power to figure out whether fraud or money laundering has been conducted and determine an appropriate course of action. While this might not seem onerous, the waste is found in the high rate of false positive alerting.
“With current technology the financial institutions are compelled to cast the widest net with the smallest of holes. and drag it through the vast ocean of financial transactions,” said Stocks. “this approach inevitably captures everything. Banks and FI’s are then forced to rely on costly people; analysts and investigators, to find the one grain of sand – or instance of a financial crime – that matters.”
Between the fatigue that leads to mistakes and the high rate of false positives that leads to burnout, there’s a high attrition rate in the field of financial crimes investigation. However, with the next generation of AML and fraud tools coming to market, Stocks finally sees a real change.
“Artificial intelligence (AI) and machine learning (ML) when fueled by high quality data sets is a game changer for both the detection of both real-time financial crimes, like fraud and crimes that are typically detected after the fact, like money laundering,” said Stocks.
Because financial transactions occur in rules-based environments AI and ML are ideally suited to detecting the pattern anomaly, or to return the earlier analogy, finding the tiny grain of sand in the net that swept the entire ocean.
“Not only do these next generation of tools reduce the number of both false negatives and false positives, but when enriched with high quality reference data that is itself, further enriched with social data, for example, they can deliver a result to analysts and investigators that can be visualized, with the fullest picture of the suspicious activity, and resolved quickly,” shared Stocks.
“In short, these data-driven tools can fix an unglamorous problem that has been a thorn in the side of financial institutions, regulators, governments and international organizations for the better part of a century.”
Ready to learn more about how to improve the accuracy and efficiency of your institutions financial? Here are some great resources.