{"product_id":"anti-money-laundering-transaction-monitoring-systems-implementation-isbn-9781119381808","title":"Anti-Money Laundering Transaction Monitoring Systems Implementation","description":"\u003cb\u003eEffective transaction monitoring begins with proper implementation\u003c\/b\u003e \u003cp\u003e\u003ci\u003eAnti-Money Laundering Transaction Monitoring Systems Implementation\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eTransaction 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eDetect anomalies in the data\u003c\/li\u003e \u003cli\u003eHandle known abnormal behavior\u003c\/li\u003e \u003cli\u003eComply with regulatory requirements\u003c\/li\u003e \u003cli\u003eMonitor transactions using various techniques\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eRegulators 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. \u003ci\u003eAnti-Money Laundering Transaction Monitoring Systems Implementation\u003c\/i\u003e is an invaluable resource for those tasked with putting these systems in place, providing clear discussion and practical implementation guidance. \u003c\/p\u003e\u003cp\u003eAbout the Authors xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 An Introduction to Anti-Money Laundering 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Emergence of AML 2\u003c\/p\u003e \u003cp\u003eAML as a Compliance Domain 5\u003c\/p\u003e \u003cp\u003eThe Objectives of AML 9\u003c\/p\u003e \u003cp\u003eRegulatory Reporting 9\u003c\/p\u003e \u003cp\u003eCorporate Citizenship versus Profitability 10\u003c\/p\u003e \u003cp\u003eAbout True and False Positives and Negatives 11\u003c\/p\u003e \u003cp\u003eThe Evolution of Automated Transaction Monitoring 15\u003c\/p\u003e \u003cp\u003eFrom Rule-Based to Risk-Based 17\u003c\/p\u003e \u003cp\u003eFrom Static to More Dynamic Transaction Monitoring 22\u003c\/p\u003e \u003cp\u003eLatest Trends: Machine Learning and Artificial Intelligence 26\u003c\/p\u003e \u003cp\u003eLatest Trends: Blockchain 29\u003c\/p\u003e \u003cp\u003eRisk-Based Granularity and Statistical Relevance 34\u003c\/p\u003e \u003cp\u003eSummary 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Transaction Monitoring in Different Businesses 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBanking 43\u003c\/p\u003e \u003cp\u003eCorrespondent Banking 46\u003c\/p\u003e \u003cp\u003eBanking – Trade Finance 49\u003c\/p\u003e \u003cp\u003eBanking – Credit Card 60\u003c\/p\u003e \u003cp\u003eInsurance 60\u003c\/p\u003e \u003cp\u003eSecurities 63\u003c\/p\u003e \u003cp\u003eStored Value Facilities (SVFs) 66\u003c\/p\u003e \u003cp\u003eCasinos and Online Gambling 68\u003c\/p\u003e \u003cp\u003eLottery and Jockey Club 70\u003c\/p\u003e \u003cp\u003eOther Businesses 72\u003c\/p\u003e \u003cp\u003eSummary 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Importance of Data 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eETL: Extract, Transform, and Load 76\u003c\/p\u003e \u003cp\u003eExtract: Data Availability and Sourcing 77\u003c\/p\u003e \u003cp\u003eTransform: Data Quality, Conversion, and Repair 80\u003c\/p\u003e \u003cp\u003eData Load and Further Processing 89\u003c\/p\u003e \u003cp\u003eLoading of the Data 89\u003c\/p\u003e \u003cp\u003eData Lineage 92\u003c\/p\u003e \u003cp\u003eMultiple ETLs 92\u003c\/p\u003e \u003cp\u003eSummary 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Typical Scenario Elements 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTransaction Types 96\u003c\/p\u003e \u003cp\u003eActionable Entity 100\u003c\/p\u003e \u003cp\u003eScenario Parameters 106\u003c\/p\u003e \u003cp\u003eUse of Maximum Instead of Minimum Value Threshold 108\u003c\/p\u003e \u003cp\u003eThreshold per Customer 109\u003c\/p\u003e \u003cp\u003ePre-Computing Data 110\u003c\/p\u003e \u003cp\u003eTimeliness of Alerts 112\u003c\/p\u003e \u003cp\u003eUse of Ratios 114\u003c\/p\u003e \u003cp\u003eRatio as Degree of Change\/Similarity 117\u003c\/p\u003e \u003cp\u003eRatio as Proportion 119\u003c\/p\u003e \u003cp\u003eOther Common Issues 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Scenarios in Detail 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLarge Aggregate Value 122\u003c\/p\u003e \u003cp\u003eUnexpected Transaction 123\u003c\/p\u003e \u003cp\u003eHigh Velocity\/Turnover 129\u003c\/p\u003e \u003cp\u003eTurnaround\/Round-Tripping 132\u003c\/p\u003e \u003cp\u003eStructuring 136\u003c\/p\u003e \u003cp\u003eEarly Termination\/Quick Reciprocal Action 141\u003c\/p\u003e \u003cp\u003eWatchlist 141\u003c\/p\u003e \u003cp\u003eCommon Specifications across Unrelated Entities 142\u003c\/p\u003e \u003cp\u003eInvolving Unrelated Third Party 144\u003c\/p\u003e \u003cp\u003eOne-to-Many 144\u003c\/p\u003e \u003cp\u003eTransacting Just below Reporting Threshold 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 The Selection of Scenarios 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSelecting Scenarios 148\u003c\/p\u003e \u003cp\u003eRegulatory Requirements 148\u003c\/p\u003e \u003cp\u003eBusiness Drivers 150\u003c\/p\u003e \u003cp\u003eData Quality and Availability of Reference Data 152\u003c\/p\u003e \u003cp\u003eMaintenance of the Scenario Repository 152\u003c\/p\u003e \u003cp\u003eHow Specific should a Scenario Rule Be? 153\u003c\/p\u003e \u003cp\u003eOverlapping Scenario Rules 155\u003c\/p\u003e \u003cp\u003eSummary 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Entity Resolution and Watchlist Matching 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEntity Resolution 158\u003c\/p\u003e \u003cp\u003eWatchlists 161\u003c\/p\u003e \u003cp\u003eSummary 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Customer Segmentation 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need for Segmenting Customers 186\u003c\/p\u003e \u003cp\u003eApproaches to Segmentation 188\u003c\/p\u003e \u003cp\u003eOverview of Segmentation Steps 191\u003c\/p\u003e \u003cp\u003eOrganizational Profiling 193\u003c\/p\u003e \u003cp\u003eCommon Segmentation Dimensions 195\u003c\/p\u003e \u003cp\u003eConsiderations in Defining Segments 197\u003c\/p\u003e \u003cp\u003eCheck Source Data for Segmentation 199\u003c\/p\u003e \u003cp\u003eVerify with Statistical Analysis 200\u003c\/p\u003e \u003cp\u003eOngoing Monitoring 205\u003c\/p\u003e \u003cp\u003eChange of Segmentation 205\u003c\/p\u003e \u003cp\u003eSummary 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Scenario Threshold Tuning 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need for Tuning 210\u003c\/p\u003e \u003cp\u003eParameters and Thresholds 210\u003c\/p\u003e \u003cp\u003eTrue versus False, Positive versus Negative 212\u003c\/p\u003e \u003cp\u003eCost 213\u003c\/p\u003e \u003cp\u003eAdapting to the Environment 214\u003c\/p\u003e \u003cp\u003eRelatively Simple Ways to Tune Thresholds 215\u003c\/p\u003e \u003cp\u003eObjective of Scenario Threshold Tuning 216\u003c\/p\u003e \u003cp\u003eIncreasing Alert Productivity 216\u003c\/p\u003e \u003cp\u003eDefinition of a Productive Alert 219\u003c\/p\u003e \u003cp\u003eUse of Thresholds in Different Kinds of Scenario Rules 220\u003c\/p\u003e \u003cp\u003eRegulation-Driven Rules 220\u003c\/p\u003e \u003cp\u003eStatistical Outlier 221\u003c\/p\u003e \u003cp\u003eInsignificance Threshold 225\u003c\/p\u003e \u003cp\u003eSafety-Blanket Rules 225\u003c\/p\u003e \u003cp\u003eCombining Parameters 226\u003c\/p\u003e \u003cp\u003eSteps for Threshold Tuning 228\u003c\/p\u003e \u003cp\u003ePreparation of Analysis Data 234\u003c\/p\u003e \u003cp\u003eScope of Data 234\u003c\/p\u003e \u003cp\u003eData Columns 234\u003c\/p\u003e \u003cp\u003eQuick and Easy Approach 237\u003c\/p\u003e \u003cp\u003eAnalysis of Dates 238\u003c\/p\u003e \u003cp\u003eStratified Sampling 239\u003c\/p\u003e \u003cp\u003eStatistical Analysis of Each Tunable Scenario Threshold Variable 239\u003c\/p\u003e \u003cp\u003ePopulation Distribution Table by Percentile (Ranking Analysis) 244\u003c\/p\u003e \u003cp\u003eDistribution Diagram Compressed as a Single Line 245\u003c\/p\u003e \u003cp\u003eMultiple Peaks 246\u003c\/p\u003e \u003cp\u003eZeros 246\u003c\/p\u003e \u003cp\u003eAbove-the-Line Analysis and Below-the-Line Analysis 247\u003c\/p\u003e \u003cp\u003eAbove-the-Line Analysis 247\u003c\/p\u003e \u003cp\u003eBelow-the-Line Analysis 249\u003c\/p\u003e \u003cp\u003eUse of Scatter plots and Interactions between Parameter Variables 251\u003c\/p\u003e \u003cp\u003eBinary Search 258\u003c\/p\u003e \u003cp\u003eWhat-If Tests and Mock Investigation 260\u003c\/p\u003e \u003cp\u003eWhat-If Tests 260\u003c\/p\u003e \u003cp\u003eSample Comparisons of What-If Tests 261\u003c\/p\u003e \u003cp\u003eQualifying Results of What-If Tests 262\u003c\/p\u003e \u003cp\u003eScenario Review Report 263\u003c\/p\u003e \u003cp\u003eScenario Review Approach 268\u003c\/p\u003e \u003cp\u003eScenario Review Results 268\u003c\/p\u003e \u003cp\u003eSummary 274\u003c\/p\u003e \u003cp\u003eIndex 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAU CHAN YIP (DEREK)\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMAARTEN VAN DIJCK NEMCSIK, LLM, P\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, 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 \u0026amp; Security Business Intelligence Unit, responsible for internal and external training courses in the financial crime and compliance space.  \u003c\/p\u003e\u003cp\u003eRegulators across the globe continuously work to require banks, insurance companies, casinos, and securities trading firms to supply automated systems that combat money laundering and other financial crimes. As the regulatory regimes have become more complex, many firms struggle with the implementation of effective solutions that will protect them from the onerous penalties that accompany violations of these regulations.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnti-Money Laundering Transaction Monitoring Systems Implementation: Finding Anomalies\u003c\/i\u003e delivers a comprehensive and insightful analysis of all facets of anti-money laundering (AML) transaction monitoring. Accomplished consultants, technologists, and authors Derek Chau and Maarten van Dijck Nemcsik offer readers a treatment of AML techniques that is not confined to specific coding strategies or individual company solutions. Instead, they provide a fulsome examination of high-level concepts in AML, like how to detect anomalies in data and examples of known abnormal behavior, before narrowing their focus toward the real-world implementation of AML techniques. \u003c\/p\u003e\u003cp\u003eReaders will learn about transaction monitoring strategies in individual industries like banking, gambling, insurance, and securities, as well as regulatory requirements in each of these spaces. The authors also discuss up-and-coming fields, like blockchain and machine learning, and the effects they might have on the AML space in the years to come. The book describes all the nuts-and-bolts of implementing AML transaction monitoring, from typical scenario elements and their granular details, to entity resolution and watchlist matching. Customer segmentation and scenario threshold tuning are also discussed at length. \u003c\/p\u003e\u003cp\u003ePerfect for compliance officers, managers, and chief financial officers, \u003ci\u003eAnti-Money Laundering\u003c\/i\u003e \u003ci\u003eTransaction Monitoring Systems Implementation\u003c\/i\u003e also belongs on the bookshelves of executives working in industries where AML is a core regulatory requirement, as well as IT employees and bankers who regularly interact with AML systems.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988745044197,"sku":"NP9781119381808","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119381808.jpg?v=1761781422","url":"https:\/\/k12savings.com\/products\/anti-money-laundering-transaction-monitoring-systems-implementation-isbn-9781119381808","provider":"K12savings","version":"1.0","type":"link"}