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Detecting Fraud and AML Violations In Real-Time for Banking, Telecom and eCommerce

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FULL WEBINAR: https://info.tigergraph.com/graph-gurus-3
This presentation is an overview of how to minimize fraud with TigerGraph. TigeGraph:
- Enables faster detection of fraud using deep link analytics.
- Modernizes your AML process with case studies across multiple industries.
- Helps you get fewer false positives in your fraud detection workflow.
TigerGraph is addressing these challenges for some of the largest corporations in the world including Alipay, Visa, Uber, China Mobile and SoftBank.

Published in: Data & Analytics
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Detecting Fraud and AML Violations In Real-Time for Banking, Telecom and eCommerce

  1. 1. Detecting Fraud & Anti- Money Laundering (AML) Violations In Real-Time Victor Lee & Gaurav Deshpande for Banking, Telecom, and eCommerce
  2. 2. © 2018 TigerGraph. All Rights Reserved Speaking Today 2 Victor Lee Director of Product Management TigerGraph victor@tigergraph.com Graph Data Mining Expert Gaurav Deshpande Vice President of Marketing TigerGraph gaurav@tigergraph.com, +1 510 388 2360 Big Data Analytics Veteran
  3. 3. © 2018 TigerGraph. All Rights Reserved Agenda 3 Fraud and Money Laundering – Scale and Complexity Addressing Fraud and Money Laundering – Traditional Approach Addressing Fraud and AML Violations in Real-Time with Deep Link Analytics Getting Started on your own journey to a more secure and compliant organization 3 2 1 4
  4. 4. © 2018 TigerGraph. All Rights Reserved Fraud Impacts Multiple Industries 4 The 2018 Global Fraud and Identity Report, Jan 2018 63% of businesses have experienced the same or more fraud losses in the past 12 months Online fraud alone costs consumers $16 Billion per year (bank and merchant costs are higher) Fraud cost consumers more than $16 billion, Feb 2018 Telecoms operators face $300bn global loss, Jan 2016 $300 Billion in global loss across Telecom from uncollected revenue and fraud in 2016
  5. 5. © 2018 TigerGraph. All Rights Reserved Impact of Money Laundering on Global Banking 5 Reuters News, Sept 27, 2017 $342 Billion - total US & EU fines on banks’ misconduct including anti-money laundering violations since 2009 Regulators fined US Bank $613 Million due to lax anti-money laundering controls US Bank fined over $600 Million, Feb 15, 2018 Compliance doesn’t pay, Bloomberg, April 11 2018 5% of transactions which firms identify as suspicious is reported to the authorities, of which 10% leads to further investigation (Just 0.5% of suspicious transactions investigated)
  6. 6. © 2018 TigerGraph. All Rights Reserved Traditional Approach to Fraud Prevention & AML Compliance 6 Transactions (Payments, Orders, calls) Customers Merchants, Drivers, .. Devices, Locations, .. Input Data Fraud Analytics Solution + Rules Engine Analysts Suspicious Activity Report (SAR) Queries for additional information Investigators BI / Investigation workflow tool Suspicious activities False positives Machine Learning Models Training Data
  7. 7. © 2018 TigerGraph. All Rights Reserved So What’s Missing? Current Approach • Data: features of entities, e.g., users, accounts, locations. Examples: • Phone-based fraud detection: frequency and duration of one-directional calls • Money laundering: size and frequency of the payment transactions, transactions with immediate neighbors • Detection: Analysts manually write rules regarding features of nodes or their immediate neighbors (1 to 2 hops). 7 Attributes/ features for a phone Abnormal model Ads model Harassment rule Scam model Good phone Ads Harassment Scam candidateScam • Performance: • False positives: too many cases to investigate, block legitimate business • False negatives: Fail to catch many fraud cases
  8. 8. © 2018 TigerGraph. All Rights Reserved Consider an Example - Phone Scam Illegally acquiring money from victims, or failing to pay a telecom company • $4.96 Billion – Compromised PBX/Voicemail Systems • $4.32 Billion – Subscription/Identity Theft • $3.84 Billion – International Revenue Share Fraud • $2.88 Billion – By-Pass Fraud • $2.40 Billion – Credit Card Fraud 8
  9. 9. © 2018 TigerGraph. All Rights Reserved Detecting Phone-Based Fraud by Analyzing Network or Graph Relationship Features 9 Good Phone Features Bad Phone Features (1) Short term call duration (2) Empty stable group (3) No call back phone (4) Many rejected calls (5) Average distance > 3 Empty stable group Many rejected calls Average distance > 3 (1) High call back phone (2) Stable group (3) Long term phone (4) Many in-group connections (5) 3-step friend relation Stable group Many in-group connections Good Phone Features 3-step friend relation /// Good phone Bad phone X X X
  10. 10. © 2018 TigerGraph. All Rights Reserved Generating New Training Data for Machine Learning to Detect Phone-Based Scam Graph with 500 Million phones and 10 Billion calls,1000s of new calls per second. Feed Machine Learning with new training data with 118 features per phone every 2 hours 10 Phone 2 Features Machine Learning Solution Phone 1 Features (1) High call back phone (2) Stable group (3) Long term phone (4) Many in-group connections (5) 3-step friend relation (1) Short term call duration (2) Empty stable group (3) No call back phone (4) Many rejected calls (5) Avg. distance > 3 Training Data Tens – Hundreds of Billions of calls
  11. 11. © 2018 TigerGraph. All Rights Reserved Evolution of Graph Technology 11 Graph 1.0 • Storage and Visualization focused • No built-in parallel computation model • Very slow loading large datasets • Cannot scale out • Not designed for real-time graph updates or queries for large datasets • Limited multi-hop analytics capabilities on large graphs (2 hops) Example – Neo4j Graph 3.0 • Better scale-out, but speed and updates are still an issue • Built on top of NoSQL repository such as Apache Cassandra • Not designed for real-time graph updates or queries for large datasets • Limited multi-hop analytics capabilities on large graphs (2 hops) • Scalability for massive datasets • Supports real-time graph updates and queries for enterprise scale • Provides deep link analytics (3-10+ hops) traversing millions of nodes and performing complex calculations • Privacy for sensitive data • Ease of use for development & deployment Graph 2.0 Example – DataStax
  12. 12. © 2018 TigerGraph. All Rights Reserved Addressing Fraud Prevention and AML compliance with Real-time Deep Link Analytics 12 Real-time Multi-Hop Performance Sub-second response for queries touching tens of millions of entities/relationships Transactional (Mutable) Graph Hundreds of thousands of updates per second, Billions of transactions per day Scalability for Massive Datasets 100 B+ entities, 1 Trillion+ relationships Ease of Development & Deployment Easy to use query language (GSQL) for rapidly developing & deploying complex analytics Privacy for Sensitive Data Control access based on user role, data type, or department Deep Link Analytics Queries traverse 3 to 10+ hops deep into the graph performing complex calculations
  13. 13. © 2018 TigerGraph. All Rights Reserved Different Types of Financial Fraud • Phone Scam • Credit Card Chargeback Fraud • Advertising/Camouflage Fraud • Money Laundering 13
  14. 14. © 2018 TigerGraph. All Rights Reserved Credit Card Chargeback Fraud • Fraudsters use stolen credit card and phone to buy product/service from a merchant • Fraudsters receive and resell product/service • Card owner realizes and cancels the stolen credit card • Fraudster walks away with the money, while bank and merchant selling the product/service bear the loss 14
  15. 15. © 2018 TigerGraph. All Rights Reserved Detecting Credit Card Chargeback Fraud • Analyze complex network of payment transactions, devices/phones and linked accounts • Find accounts that are connected to fraudulent transactions and/or devices • If connection is strong enough à shut down account to prevent further loss 15 1 2 1 2 Account 1 Account 2 Account 3 7. Chargeback 4. Unsettled 5. Settled 6. Unsettled 3. Chargeback1. Chargeback 2. Unsettled Active Account Active Account Neighbor Info • Total chargeback $ • Total Unsettled $ • Total Settled $ • # of transactions • # of settled transactions • # of unsettled transactions • # of banned credit card • #of active credit card • # of banned device • # of normal device
  16. 16. © 2018 TigerGraph. All Rights Reserved Advertising/Recommendation Fraud • Click/Impression Fraud • Pay-per-click • Pay-per-impression • Recommendation Fraud • Fake reviews or follows • Use camouflage or hijacked accounts 16
  17. 17. © 2018 TigerGraph. All Rights Reserved Detecting Advertising/Recommendation Fraud Looks like many users, but really controlled by one 17 • Use machine learning to find the difference between organic reviews/follows vs. fake • Example Features used in analytics: # common products/brands followed/purchased # products/brands not followed/purchased together # hops between accounts # timing between events # devices shared # payment instruments shared Fraudster
  18. 18. © 2018 TigerGraph. All Rights Reserved Money Laundering Transforming the profits from illegal activities and corruption into apparently “legitimate” assets. • Structuring: many small cash deposits, to avoid anti-money laundering report requirements • Bulk cash smuggling: smuggling cash to offshore financial institutions • Cash-intensive business: restaurants, casinos, etc. • Round-tripping: money deposited offshore, brought back as investment to avoid taxation 18
  19. 19. © 2018 TigerGraph. All Rights Reserved Detecting Money Laundering Violations/ Anti-Money Laundering (AML) 19
  20. 20. © 2018 TigerGraph. All Rights Reserved Detecting Money Laundering Violations/ Anti-Money Laundering (AML) 1. Start from a few initial suspicious accounts/transactions. 2. Probe upstream and downstream of money flow. 3. Ignore normal accounts. 4. Add in more “participating” accounts that may be involved in money laundering 5. Converge when the algorithm finds the “source” account and the “target” account. 20 Algorithm to Discover Money Laundering Subgraph
  21. 21. © 2018 TigerGraph. All Rights Reserved AML—Initial Suspicious Accounts 21 1. Start from a few initial suspicious accounts.
  22. 22. © 2018 TigerGraph. All Rights Reserved AML—Upstream Probe 1 22 2. Probe upstream…
  23. 23. © 2018 TigerGraph. All Rights Reserved AML—Upstream Probe 2 23 2. Probe upstream…
  24. 24. © 2018 TigerGraph. All Rights Reserved AML—Downstream Probe 1 24 2. … and downstream
  25. 25. © 2018 TigerGraph. All Rights Reserved AML—Downstream Probe 2 25 2. … and downstream
  26. 26. © 2018 TigerGraph. All Rights Reserved AML— Final Subgraph Is Returned 26 • Converge when the algorithm finds “source” account(s) and “target” account(s).
  27. 27. © 2018 TigerGraph. All Rights Reserved Detecting Various Types of Fraud with TigerGraph • Graph Features to the rescue • Integrate multiple data sources into one graph • Real-time updates • GSQL helps easily collect complex, deep-link, aggregate graph features • Feed Machine Learning algorithm with new training data • Deep Link Analytics to the rescue • GSQL easily describes graph traversal and compute patterns • Massive parallel processing for speed and efficiency • Visualization shows evidence right on the spot 27 Real-time Deep Link Analytics at Massive Scale
  28. 28. © 2018 TigerGraph. All Rights Reserved Real-Time Graph Analytics Platform 28 User Interface: • GSQL language for schema/loading/ queries/updates • REST API for connecting to other applications • GraphStudio GUI for human interaction Output: • JSON or visual graph
  29. 29. © 2018 TigerGraph. All Rights Reserved #1 e-payment company in the world, 100M daily active users #1 US payment company #1 Mobile E-commerce #1 Mobile global supply-chain #1 Power-grid company The largest transaction graph in production in the world (100B+ vertices, 2B+ daily real time updates) Business Graph Real-time Personalized Recommendation Supply-chain logistics Electrical Power Grid #1 Ride sharing company Risk and Compliance Customers 29
  30. 30. © 2018 TigerGraph. All Rights Reserved Getting Started with your own journey to a more secure and compliant organization • Download the Fraud Prevention & AML brief https://info.tigergraph.com/aml-solution-brief • Read the benchmark report comparing TigerGraph with older generation graph solutions - https://info.tigergraph.com/benchmark • See TigerGraph in action – take the test drive: https://www.tigergraph.com/try-tigergraph/ 30
  31. 31. © 2018 TigerGraph. All Rights Reserved Thank You Check us out on social media! 31 @TigerGraphDB Youtube.com/tigergraph Facebook.com/Tiger GraphDB Linkedin.com/company/Ti gerGraph

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