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Details
Table of 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
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
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
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
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
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
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
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
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
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