Artificial Intelligence Applications in Banking and Financial Services: Anti Money Laundering and Compliance

  • 2h 22m
  • Abhishek Gupta, Dwijendra Nath Dwivedi, Jigar Shah
  • Springer
  • 2023

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.

About the Author

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 McKinsey & 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 PhD 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.

In this Book

  • Acronyms
  • Overview of Money Laundering
  • Financial Crimes Management and Control in Financial Institutions
  • Overview of Technology Solutions
  • Data Organization for an FCC Unit
  • Planning for AI in Financial Crimes
  • Applying Machine Learning for Effective Customer Risk Assessment
  • Artificial Intelligence-Driven Effective Financial Transaction Monitoring
  • Machine Learning-Driven Alert Optimization
  • Applying Artificial Intelligence on Investigation
  • Ethical Challenges for AI-Based Applications
  • Setting up a Best-In-Class AI-Driven Financial Crime Control Unit (FCCU)
  • Resources