Using Advanced Data Analytics and Technology to Combat Financial Crime
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The webinar focuses on leveraging advanced data analytics and technology to combat financial crime within the money services business (MSB) market, highlighting regulatory demands and common criminal activities. Key challenges include compliance, data quality, and structuring techniques used by criminals, alongside strategies for building effective anti-money laundering (AML) programs. The discussion emphasizes the importance of an analytics-driven compliance culture to enhance detection of fraudulent activities and streamline reporting processes.
Using Advanced Data Analytics and Technology to Combat Financial Crime
- 1. TITLE WEBINAR USING ADVANCED DATA ANALYTICS & TECHNOLOGY TO COMBAT FINANCIAL CRIME WEBINAR
- 2. Mike Caluori Delivery Manager Analytics & Data Science Practice CaseWare Analytics PRESENTER
- 3. AGENDA 1. The Money Services Business (MSB) Market 2. Regulatory Environment 3. Common Crimes 4. Fines and Penalties 5. Challenges 6. Building a Successful AML Program 7. The Role of Technology 8. Data Analytics 9. Q&A
- 4. THE MSB MARKET Worldwide, 230 million people send $500 billion in remittances each year, primarily using firms like Western Union, Moneygram, and RIA, which together control 1.1 million retail locations and account for more than 25% of the world’s annual remittance volume.
- 5. EMERGING MARKET 1. Mobile money 2. P2P 3. Bitcoins 4. Social media apps (e.g. Facebook) 5. Others (e.g. Starbucks)
- 6. REGULATORY ENVIRONMENT 4 Pillars of BSA compliance 1. Compliance Officer 2. Tailored internal policies, procedures and controls 3. Ongoing, relevant training of employees 4. Independent Review for compliance
- 7. FINES AND PENATIES Civil money penalty against American Express Bank International and American Express Travel Related Services Company Inc Civil money penalty against Thomas Haider, Chief Compliance Officer and Senior Vice President of Government Affairs at MoneyGram Civil money penalty against Ripple Labs Inc. for acting as an MSB and selling its virtual currency without registering with FinCEN
- 8. COMMON CRIMES 1. Human smuggling 2. Narcotics trafficking 3. Terrorist financing 4. Elder abuse 5. Mail order bride 6. Heavenly offerings Christopher Westphal, Data Mining for Intelligence, Fraud and Criminal Detection, 2009
- 9. CHALLENGES 1. Reporting/Filing requirements 2. Staffing 3. Margins 4. Data quality 5. De-risking
- 10. HOW TO BUILD A SUCCESS AML PROGRAM 1. Governance and tone 2. Training 3. Risk management 4. Compliance technologies 5. Culture
- 12. CASE MANAGEMENT
- 13. FILING IS COSTLY
- 14. END-TO-END AUTOMATION 70%-80% of your filing can be done without human interactions 1. Detect reportable transactions 2. Aggregate the data 3. Complete the reports 4. Validate 5. Submit 6. Confirm 7. Evidence
- 15. DATA ANALYTICS 1. Getting the data right 2. Analytics 3. From monitoring to insights 4. Realizing the value
- 16. THE DATA CHALLENGE 1. Existence 2. Completeness 3. Validity 4. Accuracy
- 17. DATA REFINEMENT 1. Parsing 2. Standardizing 3. Fuzzy matching
- 18. EXAMPLE Standardizing Best of Breed Record Parsing Source House Street Suffix City Zip Code State Country 234 Leader Ave, Denver, Colorado, 80327, United States 234 Leader Ave Denver 80327 Colorado United States 234 Leadr Avenue, Denver 234 Leadr Avenue Denver Colorodo Leader St., Denver, USA Leader St. Denver USA Source Number Street Suffix City Zip Code State Country 234 Leader Ave, Denver, Colorado, 80327, United States 234 Leader Avenue Denver 80327 Colorado USA 234 Leadr Avenue, Denver 234 Leader Avenue Denver 80327 Colorado USA Leader St., Denver, USA Leader Avenue Denver 80327 Colorado USA Source Number Street Suffix City Zip Code State Country 234 Leader Avenue, Denver, Colorado, 80327, USA 234 Leader Avenue Denver 80327 Colorado USA
- 19. VIRTUAL CUSTOMER ID 1. Fuzzy matching 2. A unique ID 3. This allows us to create a virtual single instance of the customer Virtual ID Name Address ID Presented Certainty 100900212 Andrew Simpson 234 TRELAWNEY CIRCLE CONYERS GA 30013 DL#0**~*33 100 100900212 Andrew Simpson 234 TRELAWNEY ROAD CONYERS GA 30013 95 100900212 A. Simpson DL#0**~*33 90
- 20. DRIVEN BY RISK ASSESSMENT 1. What to monitor? 2. Frequency? 3. Focus on highest risk
- 21. SCENARIOS
- 22. STRUCTURING 1. Multiple transactions by a customer across locations to evade CTR filings 2. Individually below threshold but cumulatively above Hints Benefits from refined customer and household data. Exclude noise like PO Boxes, blanks, or invalid addresses. Make the time period for review a variable (i.e.: don’t just use a day).
- 23. STRUCTURING 3. Multiple transactions at single location across multiple days
- 24. SMURFING 1. Multiple transactions by multiple customers across locations to evade CTR filings 2. Individually below threshold but cumulatively above 3. May be to the same recipient Hints Isolate all the suspected smurfs, and then examine receivers for trends. Correlate the receiver with information from your network analysis.
- 25. FLIPPING 1. Receives a transaction and quickly resends or vice-versa 2. May keep a small portion (“fee”) Hints Make the timeline for the flip a variable and do “what-if” to see what works best. Make a distinction between a fee taken or not. Then examine other inbounds that are not flipped.
- 26. MULES 1. Sending from and receiving at the same location Hints Suggest you limit this to same location or city so as to not mix with flipping. Make a distinction between a fee taken or not. Then examine other inbounds.
- 27. NETWORKS 1. Layering evidenced by a collection of smurfing, flipping, mules, etc. 2. Correlation between senders and receivers Hint There are some good tools available to do this. However you can achieve something success with just summaries.
- 28. MULTIPLE CUSTOMER – SAME ADDRESS Hints Benefits from refined data. Exclude noise like P.O. Boxes, blanks or invalid addresses. Prioritize by number of customers or amounts sent or frequency.
- 29. SINGLE CUSTOMER – MULTIPLE ADDRESS Hints Benefits from refined data. Reduce false positives by using more than just name (e.g.: ID). Prioritize by number of addresses or amounts sent or frequency.
- 30. INHERENTLY RISKY 1. Identifying transactions to and from “high” risk countries. 2. Refer to Financial Action Task Force (FATF) website – fatf.org
- 31. ELDER ABUSE 1. Treat transfers from elderly customers as higher risk 2. Also correlate to high risk payout locations/recipients 3. HQ Compliance should detect any structuring Hints Make the age variable by sex. Flag known recipients and trigger a red flag on future transfers.
- 32. NGOS/CHARITIES/RELIGIOUS 1. Use keywords to detect 2. Treat as higher risk transactions 3. May be used for tax evasion and money laundering Hints Adjust thresholds for detection based on existing cases. Add new keywords over time.
- 33. COLLUSION DETECTION Trending by locations and employee: 1. Falsified addresses (“ANYWHERE STREET”) 2. Invalid or duplicated IDs 3. Invalid or duplicated SSN
- 34. INSIGHTS 1.What locations am I having a challenge with? 2.What is the most frequent anomaly detected? 3.What controls are failing?
- 35. ROOT CAUSE AND ACTION Why did we allow the structuring? • Was not aware of the requirements • Not structuring (false positive) • Scared of customer What actions were taken? • Filed a SAR • Requested additional details • Rejected transfer
- 36. ADVANCED ANALYTICS 1.Anomaly detection 2.Link analysis 3.Predictive
- 38. ANOMALY DETECTION 1.Anomaly detected based on Customer’s historical behavior 2.Peer anomaly based on behavior that is significantly different from customers in the same segment/cluster 3.Primarily focused on these attributes: • Frequency • Value • Source and destinations • Time and day • Segmentation (e.g. location)
- 39. NETWORK LINKING 1. Anomaly detected based on Customer’s historical behavior 2. Peer anomaly based on behavior that is significantly different from customers in the same segment/cluster 3. Primarily focused on these attributes: • Frequency • Value • Source and destinations • Time and day • Segmentation (e.g. location)
- 40. PREDICTIVE ANALYTICS 1. Using known cases to identify the procurement pattern that normally results in fraud 2. Using machine learning to train models and predict outcomes
- 42. KEY TAKE-AWAYS • The rewards and risks for MSBs are big • The only way to maintain compliance, manage volume of work at a reasonable cost, and mitigate risks is to have the right culture and an analytics-driven compliance program • The right data analytics program which combines anomaly detection, network linking and predictive analytics can identify many money-laundering scenarios quickly and easily • Identify root causes that can lead to potential cost-saving opportunities in your processes • Start taking Cross Jurisdictional Reporting Requirements into account
- 43. QUESTIONS
- 45. TITLE WEBINAR USING ADVANCED DATA ANALYTICS & TECHNOLOGY TO COMBAT FINANCIAL CRIME WEBINAR