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
