Artificial Intelligence Applications in Banking and Financial Services
Anti Money Laundering and Compliance
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Beschreibung
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.
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.
Includes AI studies that impact entire anti-money laundering and compliance process in banks and financial institutions Covers entire AI journey from Data to decisioning in bringing AI-driven efficiency in AML compliance Contains pointers for both starter and practitioners using multiple AI-driven use cases from field experience
Autor*in
Abhishek Gupta
Themen in »Artificial Intelligence Applications in Banking and Financial Services«
Anti-money laundering Anomaly detection Predictive modelling financial crime Individual and Non-Individual money laundering Reducing false positives in AML AML solutions overview AI in AML Chief compliance officer handbook Alert optimization Implementing RPA in AML Improving investigation effectiveness Data governance for AML preparing data for machine learning in AML
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“Artificial intelligence applications in banking and financial services arrives as a timely and authoritative guidebook for financial institutions seeking to harness advanced technologies against sophisticated criminal threats. Authored by Abhishek Gupta, Dwijendra Nath Dwivedi, and Jigar Shah, this meticulously structured book serves not just as a theoretical primer, but more crucially, as a practical handbook for banking and compliance professionals, providing actionable insights and integration strategies for leveraging artificial intelligence(AI) in financial crime prevention.” (Goran Trajkovski, Computing Reviews, June 13, 2024)
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