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.
表中的内容
Chapter 1: Introduction to financial crimes and its participants.- Chapter 2: Anti financial crimes organization overview in a financial institution.- Chapter 3: Financial institutions approach to curbing and mitigating financial crimes.- Chapter 4: IT solutions for monitoring and managing financial crimes.- Chapter 5: Typical challenges faced by AML and compliance divisions.- Chapter 6: Applications of artificial intelligence and digitization in financial crimes.- Chapter 7: Data organization and governance in financial crimes.- Chapter 8: Machine learning approach to customer due diligence and watchlist monitoring.- Chapter 9: Applying machine learning for transaction monitoring to optimize false positives.- Chapter 10: application of network analysis to further improve detection of financial crimes.- Chapter 11: AML investigation and application of digitization and machine learning for saving investigation time.- Chapter 12: Futuristic enterprise level AI driven Financial Crime Investigation unit (FCU) for a financial institution.
关于作者
Abhishek Gupta possess over 18 years of experience in analytics driven advisory, with focus on enterprise-wide risk management, forensics for financial crimes and corporate strategy. Abhishek was also the risk management expert for Mc Kinsey & Co. and then with Sutra Management Consultancies, where he has successfully worked with over 30 banks and financial institutions on Risk and Compliance offerings, South East Asia, North America and Europe. Abhishek has been working with his team on new emerging technologies like text analytics, voice and image analytics. Academically, he has also been one of the co-inventors of a provisional patent on fraud management technology in India, authored few research papers in reputed journals and has been a visiting faculty for MBA colleges.
Dwijendra Nath Dwivedi is having over 17 years of experience in applying Artificial Intelligence and Advanced Analytics across different industries, e.g.BFSI, Government, Telco, and utilities in various functional areas, e.g. Risk and marketing. He conducts AI Value seminars and workshops, for the executive audience and for power users. He is currently leading Analytics and AI practice for EMEA at SAS and helps to enable organizations in applications of AI. As a thought leader, he is bridging the gap between business needs and analytical enablers and to drive analytical thinking into successful business strategies. He completed his MPhil. from Indira Gandhi Institute of Development and research. He is currently pursuing his Ph D in AI from the Department of Economics and Finance from Krakow University of Economics.
Jigar Shah is a techno-management professional with 12 years of work experience into BFSI domain in business and analytics, consulting, IT services, project management and private equity. He carries hands-on experience in executing challenging assignments and consulting clients in areas of financial risk, compliance, and business intelligence. He has a rich experience in working with teams and clients across geographies.
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