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This book discusses all aspects of money laundering, starting from
traditional approach to financial crimes to artificial
intelligence-enabled solutions. It also discusses the regulators
approach to curb financial crimes and how syndication among
financial institutions can create a robust ecosystem for monitoring
and managing financial crimes. It opens with an introduction to
financial crimes for a financial institution, the context of
financial crimes, and its various participants. Various types of
money laundering, terrorist financing, and dealing with watch list
entities are also part of the discussion. Through its twelve
chapters, the book provides an overview of ways in which financial
institutions deal with financial crimes; various IT solutions for
monitoring and managing financial crimes; data organization and
governance in the financial crimes context; machine learning and
artificial intelligence (AI) in financial crimes; customer-level
transaction monitoring system; machine learning-driven alert
optimization; AML investigation; bias and ethical pitfalls in
machine learning; and enterprise-level AI-driven Financial Crime
Investigation (FCI) unit. There is also an Appendix which contains
a detailed review of various data sciences approaches that are
popular among practitioners. The book discusses each topic through
real-life experiences. It also leverages the experience of Chief
Compliance Officers of some large organizations to showcase real
challenges that heads of large organizations face while dealing
with this sensitive topic. It thus delivers a hands-on guide for
setting up, managing, and transforming into a best-in-class
financial crimes management unit. It is thus an invaluable resource
for researchers, students, corporates, and industry watchers alike.
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