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Applying AI in anti-money laundering operations
Although stakes of financial crime compliance operations have risen greatly, traditional technology used in compliance operations has reached an impasse. Rules-based technology and siloed operations are proving to be inadequate in detecting hidden risks, and financial institutions are drowning in alerts. A flood of false positives and heavy reliance on manual processes are making anti-money laundering (AML) programs costly, inefficient and unsustainable. This paper discusses how artificial intelligence (AI) and machine learning (ML)powered solutions have the potential to solve the current challenges in AML. Theycan enable institutions to adopt a more informed and risk-based approach and ensure thatmost critical attributes and scenarios are fed into a detection engine that has finely tunedparameters and thresholds. They can also help generate optimal number of high-qualityalerts that are prioritised according to risk. Case management efficiency and effectivenesscan be enhanced by incorporating AI and ML techniques while learnings can be fed backinto all stages for continuous improvements. The paper analyses how these will allowfinancial institutions to manage money laundering risks proactively and holistically, reducingcosts, inefficiencies and chances of fines. AML departments at financial institutions havestarted dipping their toes in the pool of advanced analytics. Typically, they start with tacticalAI adoption in one or a few areas; success in early stages should expedite further adoption.
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Arin Ray is a senior analyst with Celent’s Securities & Investments practice and is based in the company’s New York office. Arin has published research reports and consulted clients on technological and operational issues related to trading, post-trade and risk and compliance functions. He has presented at key industry events and his research has been frequently cited in the financial media and trade journals.