Abstract
Money laundering (ML) is a serious problem for the economies and financial institutions around the world. Financial institutions get used by organized criminals and terrorists as vehicles of large-scale money laundering, which presents the institutions with challenges of regulatory compliance, maintaining financial security, preserving goodwill and reputation, and avoiding operational risks like liquidity crunch and lawsuits. Hence prevention, detection, and control of ML is crucial for the financial security and risk management of financial institutions. Realizing the gravity of ML, various nations have started anti-ML (AML) activities, along with cooperative international efforts, including Financial Action Task Force, Egmont Group and Wolfsberg Group. This chapter begins with an overview of ML, discusses commonly used methods of ML, and the anti-ML efforts worldwide. After surveying some analytics techniques used to estimate the extent of ML, some data-mining techniques reported in the literature for detection of ML episodes (instances) are surveyed.
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