Strengthen Your AML Compliance Program with Data Mining
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The document outlines a webinar on strengthening AML compliance using data mining, presented by Rory Barrett from Symptai Consulting. It discusses the challenges of data overload, the importance of mining data, and offers a structured approach to data preparation and model creation to enhance fraud detection across various industries. Key takeaways emphasize the need for businesses to adapt to changing technologies and leverage data mining for improved insights and risk management.
Strengthen Your AML Compliance Program with Data Mining
- 1. STRENGTHEN YOUR AML COMPLIANCE PROGRAM WITH DATA MINING WEBINAR
- 2. PRESENTER Rory Barrett Business Analyst, Project Manager Symptai Consulting
- 3. SYMPTAI CONSULTING • An industry leader in technology solutions for audit, security, business process controls, and compliance • Founded in 1998, over 150 clients • Successfully implemented CaseWare AML monitoring solutions throughout the Caribbean
- 4. AGENDA • What is Data Mining? • Impact • Start Mining Your Data • Applications in Industry
- 5. AGENDA • What is Data Mining? • Impact • Start Mining Your Data • Applications in Industry
- 6. WHAT IS DATA MINING? Data can be found: • Customer Relationship Management (CRM) • Human Resource Management (HRM) • Enterprise Resource Planning (ERP) • Application Software • Excel
- 7. PROBLEM: THE DATA EXPLOSION
- 8. PROBLEM: THE DATA EXPLOSION “We are drowning in information but starved for knowledge.” Naisbitt, J. (1982). Megatrends: Ten New Directions Transforming Our Lives. We are drowning in DATA but starved for INSIGHT
- 9. CHALLENGES • Keeping up with technology • Increasing amounts of data being generated • Analysts can only work on 100’s of cases daily
- 10. AGENDA • What is Data Mining? • Impact • Start Mining Your Data • Applications in Industry
- 11. YOU ARE UNDER INVESTIGATION • Law enforcement claims your organization is involved in money laundering activities • Your customer is suspected of funding illegal weapons purchases through her account Stacy Valdez – Age: 72 years old – Marital Status: Married – Occupation: Retired Nurse – Customer Relationship: 32 years
- 12. NO REASON TO SUSPECT STACY • Stacy has not met thresholds • Stacy is not on watch lists • Stacy has not triggered alarms
- 13. AGENDA • What is Data Mining? • Impact • Start Mining Your Data • Applications in Industry
- 14. MINING YOUR DATA: MAIN STEPS 1. Identify your business objectives 2. Access your data sources 3. Prepare the data 4. Create the model 5. Test and deploy
- 15. • What are my business challenges? • What do I want to achieve? IDENTIFY YOUR BUSINESS OBJECTIVES
- 16. ACCESS YOUR DATA SOURCES • Data comes in many different forms
- 17. PREPARE THE DATA • Get to know your data – Assess the data quality – Find the data that is useful for your needs
- 18. CREATE THE MODEL • The model will define how you use the data
- 19. TEST AND DEPLOY
- 22. NEURAL NETS
- 23. BENEFITS • Savings on time, manpower and costs • Solve problems in real-time • Fraud prediction • More accurate data • Better informed decisions • Faster information for faster action • Data mining analyses thousands of risk patterns instantly
- 25. DATA MINING ALERTS FOR RECENTLY ACTIVATED ACCOUNTS • Stacy Valdez reactivates her dormant account on May 2014 • Stacy deposits $100,000 in her reactivated account • Her payments are made to 3 different computer stores • Payment are made to a different store every 2-4 weeks • Payments range between $60,000US-$90,000US • Stacy continues to make deposits for a number of weeks
- 26. STACY IS NOT ACTING ALONE • 11 other accounts are flagged as making payments to the same group of computer stores • All accounts were previously dormant from customers over the age of 65 • Total payments estimate $15,000,000+ per year!
- 27. We are drowning in DATA but starved for INSIGHT
- 28. AGENDA • What is Data Mining? • Impact • Start Mining the Data • Applications in Industry
- 29. DATA MINING IN OTHER INDUSTRIES Industry • Finance • Insurance • Telecom • Healthcare • Transport • Consumer Goods • Research • Utilities Application • Credit Card Analysis • Claims, Fraud Analysis • Call record analysis • Patient Diagnostic Analysis • Logistic Management • Promotion Analysis • Image, Video, Speech Analysis • Power Usage Analysis
- 30. DATA MINING EXAMPLES • Data Quality Management – Data Cleansing – Data Warehousing
- 31. DATA MINING EXAMPLES • Predictive Modelling – Artificial Intelligence
- 32. DATA MINING EXAMPLES • Business Intelligence – Dashboards – Visualizations
- 33. KEY TAKEAWAYS • Bridge the big data gap • Changes in technology introduce new levels of risk to fraud • Data mining helps you see what may be missing • Adapt to ever changing threats • Pattern matching to detect fraud • Different data models to suit different needs • Applicable to virtually any industry
- 34. Embrace your DATA and regain your INSIGHT
- 36. STRENGTHEN YOUR AML COMPLIANCE PROGRAM WITH DATA MINING WEBINAR