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[DSC Europe 22] Anti-Money Laundering ML Modeling approach - Gizem Akar

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[DSC Europe 22] Anti-Money Laundering ML Modeling approach - Gizem Akar

  1. 1. Anti-Money Laundering ML Modelling approach Gizem Akar November 2022
  2. 2. 1. AML Problem Statement 2. Set – Up: Basic Idea 3. Results / Success Factors / Next Steps Footer 2 Content
  3. 3. • What is money laundering? • According to Financial Action Task Force(FATF), the goal of many criminal acts is to generate a profit for the individual or group that carries out the act. Money laundering is the processing of these criminal proceeds to disguise their illegal origin. • Corporates • Many reasons for occurring money laundering such as black money, which earned illegal way, tax evasion like hiding the expenses of the organization. • Countries • The United Nations Office on Drugs and Crime (UNODC) estimates that between 2 and 5% of global GDP is laundered each year. That’s between EUR 715 billion and 1.87 trillion each year. • In 2009, the estimated global success rate of money laundering controls was a mere 0.2% (according to the UN and US State Department) • Damages long-term economic development by distorting capital flows and the economy’s international trade (Bartlett, 2002). Footer 3 Background – Money Laundering
  4. 4. • How is it done? -Alerts are generated (based on predefined scenarios) -Alerts are distributed to Anti-Money Laundering Officer and are analyzed -Confirmed alerts are reported to the authorities(Suspicious Activity Reports) • Business Problem: -High Number of False Positives -Cases which are not detected by scenarios -Subject to human bias Footer 4 Compliance Overview
  5. 5. AML ML PoC Modelling Approach 5 Set-Up UC 1: Classification Problem Supervised Machine Learning MODEL IS TRAINED BASED ON HISTORICAL DATA 1 NORMAL SUSPICIOU S CUSTOMER S NEW ALERTS CAN BE CLASSIFIED AUTOMATICALLY 2 ALERT X SUSPICIOUS (86% CONFIDENCE) Alert X UC 2: Peer Group comparison Detect outliers within peer groups indicating suspicious activities with unsupervised Machine Learning suspicious normal suspicious Supervised Machine Learning • Target group: Alerted customers (Suspicious Activity Report) Historical Customer, Account & Transaction data Unsupervised Machine Learning • Target group: Non-alerted Customers
  6. 6. • Instead of ‘transactional view’ • ‘customer view’ has been constructed. AML ML PoC Modelling Approach 6 Data Construction Customer Transaction Alert Feature creation (e.g. transaction aggregations) Analytical Record on customer level Database or data table Transformation step DATA Standardization and Harmonization Feature selection Alert scoring model Peer Group comparison model … ML model X Simplified workflow for Analytical Record creation Account
  7. 7. Use Case Goal: • Based on a feature set comparing a customer’s behavior with his historic transaction pattern, data driven peer groups, and specific transaction categories, outliers are identified by use of unsupervised Machine Learning techniques. • The goals of the use case is to provide a (color) coding for clients into high-risk, medium-risk, and low-risk clients, with the goal that • 50% of clients categorized as high-risk, are confirmed to be ‘relevant’ • 33% of clients categorized as medium-risk should correspond to ‘unusual transactional behavior’. • The low-risk category should ideally have no suspicious behavior. Footer 7 Unsupervised Learning Modelling Hypothesis and Rationale: • The modelling hypothesis is that suspicious behavior in the AML context is correlated with transaction data that strongly deviate from what is "typical" for a certain set of customers.
  8. 8. • To be able to detect customers showing "outlier" behavior specific feature categories are constructed: • Customers current transaction profile • Customers transaction profiles are compared to the average transaction profile of their peer-groups (industry classification, company form) – homogeneous transactional behavior • Customers present transaction profile is compared to the transaction profile in the past, to detect sudden changes in the transaction behavior Footer 8 Unsupervised Learning
  9. 9. The score of each customer is determined by calculating the following metrics for each data point after applying PCA. Footer 9 Unsupervised Learning
  10. 10. • Customers with unexpected behavior will score high in score and/or orthogonal loss. • Hence, customers far away from main clusters can represent potential SARs. • 1% - Red Zone • 3% - Yellow Zone • 96%- Green Zone Footer 10 Result - Unsupervised Learning
  11. 11. • Thank you! Footer 11

Notas del editor

  • Unsupervised Machine Learning models overcome the draw-back of supervised ML models, which detect only patterns they have been trained on. Unsupervised ML, on the other hand, can detect new AML topologies, where supervised models may fail to highlight suspicious activities.(slide 5)
  • Week0_debit_domestic_avg, Week0-by-avg-Week1-Week2-Week3_debit_cash_sum,

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