Anti-Money Laundering Transaction Monitoring Systems Implementation: Finding Anomalies

ISBN-13: 9781119381808 / Angielski / Twarda / 2020 / 304 str.

Maarten van Dijck Nemcsik

Opis

Effective transaction monitoring begins with proper implementation Anti-Money Laundering Transaction Monitoring Systems Implementation provides comprehensive guidance for bank compliance and IT personnel tasked with implementing AML transaction monitoring. Written by an authority on data integration and anti-money laundering technology, this book offers both high-level discussion of transaction monitoring concepts and direct clarification of practical implementation techniques. All transaction monitoring scenarios are composed of a few common elements, and a deep understanding of these elements is the critical factor in achieving your goal; without delving into actual code, this guide provides actionable information suitable for any AML platform or solution to help you implement effective strategies and ensure regulatory compliance for your organization. Transaction monitoring is increasingly critical to banking and business operations, and the effectiveness of any given solution is directly correlated to its implementation. This book provides clear guidance on all facets of AML transaction monitoring, from conception to implementation, to help you:

• Detect anomalies in the data
• Handle known abnormal behavior
• Comply with regulatory requirements
• Monitor transactions using various techniques

Regulators all over the world are requiring banks and other companies to institute automated systems that combat money laundering. With many variables at play on both the transaction side and the solution side of the equation, a solid understanding of AML technology and its implementation is the most critical factor in successful detection. Anti-Money Laundering Transaction Monitoring Systems Implementation is an invaluable resource for those tasked with putting these systems in place, providing clear discussion and practical implementation guidance.

Spis treści

About the Authors xiiiAcknowledgments xvPreface xviiChapter 1 An Introduction to Anti-Money Laundering 1The Emergence of AML 2AML as a Compliance Domain 5The Objectives of AML 9Regulatory Reporting 9Corporate Citizenship versus Profitability 10About True and False Positives and Negatives 11The Evolution of Automated Transaction Monitoring 15From Rule-Based to Risk-Based 17From Static to More Dynamic Transaction Monitoring 22Latest Trends: Machine Learning and Artificial Intelligence 26Latest Trends: Blockchain 29Risk-Based Granularity and Statistical Relevance 34Summary 36Chapter 2 Transaction Monitoring in Different Businesses 39Banking 43Correspondent Banking 46Banking - Trade Finance 49Banking - Credit Card 60Insurance 60Securities 63Stored Value Facilities (SVFs) 66Casinos and Online Gambling 68Lottery and Jockey Club 70Other Businesses 72Summary 72Chapter 3 The Importance of Data 75ETL: Extract, Transform, and Load 76Extract: Data Availability and Sourcing 77Transform: Data Quality, Conversion, and Repair 80Data Load and Further Processing 89Loading of the Data 89Data Lineage 92Multiple ETLs 92Summary 93Chapter 4 Typical Scenario Elements 95Transaction Types 96Actionable Entity 100Scenario Parameters 106Use of Maximum Instead of Minimum Value Threshold 108Threshold per Customer 109Pre-Computing Data 110Timeliness of Alerts 112Use of Ratios 114Ratio as Degree of Change/Similarity 117Ratio as Proportion 119Other Common Issues 120Chapter 5 Scenarios in Detail 121Large Aggregate Value 122Unexpected Transaction 123High Velocity/Turnover 129Turnaround/Round-Tripping 132Structuring 136Early Termination/Quick Reciprocal Action 141Watchlist 141Common Specifications across Unrelated Entities 142Involving Unrelated Third Party 144One-to-Many 144Transacting Just below Reporting Threshold 145Chapter 6 The Selection of Scenarios 147Selecting Scenarios 148Regulatory Requirements 148Business Drivers 150Data Quality and Availability of Reference Data 152Maintenance of the Scenario Repository 152How Specific should a Scenario Rule Be? 153Overlapping Scenario Rules 155Summary 156Chapter 7 Entity Resolution and Watchlist Matching 157Entity Resolution 158Watchlists 161Summary 184Chapter 8 Customer Segmentation 185The Need for Segmenting Customers 186Approaches to Segmentation 188Overview of Segmentation Steps 191Organizational Profiling 193Common Segmentation Dimensions 195Considerations in Defining Segments 197Check Source Data for Segmentation 199Verify with Statistical Analysis 200Ongoing Monitoring 205Change of Segmentation 205Summary 207Chapter 9 Scenario Threshold Tuning 209The Need for Tuning 210Parameters and Thresholds 210True versus False, Positive versus Negative 212Cost 213Adapting to the Environment 214Relatively Simple Ways to Tune Thresholds 215Objective of Scenario Threshold Tuning 216Increasing Alert Productivity 216Definition of a Productive Alert 219Use of Thresholds in Different Kinds of Scenario Rules 220Regulation-Driven Rules 220Statistical Outlier 221Insignificance Threshold 225Safety-Blanket Rules 225Combining Parameters 226Steps for Threshold Tuning 228Preparation of Analysis Data 234Scope of Data 234Data Columns 234Quick and Easy Approach 237Analysis of Dates 238Stratified Sampling 239Statistical Analysis of Each Tunable Scenario Threshold Variable 239Population Distribution Table by Percentile (Ranking Analysis) 244Distribution Diagram Compressed as a Single Line 245Multiple Peaks 246Zeros 246Above-the-Line Analysis and Below-the-Line Analysis 247Above-the-Line Analysis 247Below-the-Line Analysis 249Use of Scatter plots and Interactions between Parameter Variables 251Binary Search 258What-If Tests and Mock Investigation 260What-If Tests 260Sample Comparisons of What-If Tests 261Qualifying Results of What-If Tests 262Scenario Review Report 263Scenario Review Approach 268Scenario Review Results 268Summary 274Index 277

O autorze

CHAU CHAN YIP (DEREK) is Principal Technical Consultant at SAS Hong Kong since 2010. He was formerly a Technology Consultant at Hewlett Packard. He specializes in data integration and anti-money laundering. He received his Master of Science degree in Computer Science from the Chinese University of Hong Kong.MAARTEN VAN DIJCK NEMCSIK, LLM, PHD, has worked with SAS since 2012 as a financial crime and tax compliance domain expert and solution lead. He is part of the SAS Global Fraud & Security Business Intelligence Unit, responsible for internal and external training courses in the financial crime and compliance space.