Anti-Money Laundering Transaction Monitoring Systems Implementation: Finding Anomalies(Wiley and SAS Business Series)

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.

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

About the Author :
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.