| Description |
1 online resource (147 p.) |
| Series |
Future of Business and Finance |
|
Future of business and finance.
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| Contents |
Intro -- Acknowledgments -- Contents -- About the Authors -- Acronyms -- 1 Overview of Money Laundering -- 1.1 Introduction -- 1.2 Overview of Various Type of Money Laundering -- 1.3 Mechanism for Laundering Money -- 1.3.1 Financial Crimes Combatting in the USA -- 1.3.2 Financial Crimes Regulatory Evolution in the EU -- 1.4 Financial Institution's Response to Combatting Money Laundering and Terrorist Financing -- 1.5 The Outcome of Increasing Focus on Financial Crimes -- References -- 2 Financial Crimes Management and Control in Financial Institutions -- 2.1 Introduction |
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2.1.1 Governance Structure -- 2.1.2 Active Monitoring of Financial Crime Events -- 2.2 Organization Design for Financial Crimes -- 2.2.1 Customer Risk Assessment -- 2.2.2 Sanctions Monitoring -- 2.2.3 Financial Transaction Monitoring from an AML Perspective -- 2.2.4 Ongoing Customer Risk Assessment -- 2.2.5 Regulatory Reporting -- 2.2.6 Legal Team -- 2.2.7 Cybersecurity Team -- 2.3 Reporting Structure in Financial Organization -- 2.4 Performance Management for the Compliance Team -- 2.4.1 Efficiency of Monitoring Systems in Place -- 2.4.2 Organization Efficiency -- 2.4.3 Operational Parameters |
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2.4.4 Regulatory and Internal Audit Reviews -- 3 Overview of Technology Solutions -- 3.1 Introduction -- 3.2 Modules of the Solution -- 3.2.1 Customer Onboarding Solution-KYC Risk Scoring -- 3.2.2 Financial Transaction Monitoring -- 3.2.3 Case Investigations -- 3.2.4 Reporting -- 3.3 Backend or Technical Functionalities -- 3.4 Organizations' Needs from AML Solutions -- 3.5 Market Overview of AML -- 3.6 Emerging Trends in AML Solutions -- 4 Data Organization for an FCC Unit -- 4.1 Introduction -- 4.1.1 Data Quality -- 4.1.2 Presence of Outliers -- 4.1.3 Data History |
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4.2 Data Dimensions Relevant for Analysis on Financial Crimes -- 4.3 Cross-Border Data-Related Challenges -- 4.4 GDPR-Related Data Challenges -- 4.5 Areas of Improvement for Creating Best-in-Class Data Organization -- 4.6 Knowledge of AI and Its Enablers for Compliance Heads -- 4.7 Improving Data Quality and Integrity in KYC -- 4.8 Desiloing of Data -- 4.9 Having the Right Lead for Data Organization -- 4.10 Evolution of Best-in-Class Data Organization -- 5 Planning for AI in Financial Crimes -- 5.1 Introduction -- 5.2 Forces Shaping the FCC Ecosystem |
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5.3 Pitfalls to Avoid When Designing AI-Enabled FCC Organization -- 5.4 Setting up Roadmap for AI Organization -- 5.5 Building Blocks of a Best-in-Class AI-Enabled Compliance Function -- 6 Applying Machine Learning for Effective Customer Risk Assessment -- 6.1 Introduction -- 6.2 Know Your Customer (KYC) Processing -- 6.2.1 Automated Alerts for Expiry and Renewals -- 6.2.2 Automation of Information Extraction -- 6.2.3 Computer Vision Application on e-KYC -- 6.3 Sanctions and Watchlist Monitoring -- 6.4 Expectations from Compliance -- 6.4.1 Challenges for Name Screening |
| Summary |
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 |
| Notes |
6.4.2 Approaches for Name Screening |
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Description based on online resource; title from digital title page (viewed on August 23, 2023) |
| Subject |
Artificial intelligence -- Financial applications.
|
|
Banks and banking -- Data processing.
|
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Artificial intelligence -- Financial applications
|
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Banks and banking -- Data processing
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| Form |
Electronic book
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| Author |
Dwivedi, Dwijendra Nath
|
|
Shah, Jigar
|
| ISBN |
9819925711 |
|
9789819925711 |
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