Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Next Generation Fraud Solutions using Neo4j

Stefan Kolmar, Neo4j

  • Login to see the comments

  • Be the first to like this

Next Generation Fraud Solutions using Neo4j

  1. 1. How to build Next Generation Fraud Solutions with Neo4j Stefan Kolmar, VP Field Engineering May 2018
  2. 2. Agenda ● Solutions using Neo4j ● Fraud ● The challenge ● How to benefit from Graph Technology ● How to build the Solution ● Conclusions
  3. 3. Solutions: new mindset required?
  4. 4. Solutions: new mindset Yesterday: - Static Applications - Designed to fulfill current requirements - Performance Constraints - Domain experts versus IT experts Tomorrow: - Flexible Applications - Designed to fulfill tomorrows requirements - Performance is not limiting - Domain experts work hand in hand with IT experts
  5. 5. Evolution using Neo4j Neo4j Platform Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists 3rd Party Tools “The Graph Advantage” Domain know-how Professional Services PS Packages Graph Based Solution
  6. 6. Neo4j based Solutions Neo4j Graph Based Solutions - Neo4j DB / Platform - Data Integration Platform - Blueprint Datamodel - Blueprint Architecture - Domain know how - Professional Services
  7. 7. Evolution using Neo4j Neo4j enables Graph Based Solutions with a need for: - Agility - Intuitiveness - High Performance to support connected data scenarios - Scalable on traversing through connected data
  8. 8. Speed: Real time query enabled Graph Based Solutions Enables Up-Sell / Cross-sell Key Features Added Value 360 degree view on data Using data Connections as a value Intuitive: Supports Business Needs Flexible: enabled for additional requirements Finding patterns within the data Detect anomalies Prevent rather than detect Enables conversation across Functions Comply to regulations What-if Analysis Telco OSS GDPR Fraud Telco BSS Recomm endations MDM Resource efficient
  9. 9. Fraud Detection Building Powerful Solutions to prevent Fraud Based on Neo4j
  10. 10. The Impact of Fraud The payment card fraud alone, constitutes for over 16 billion dollar in losses for the bank-sector in the US. $16Bpayment card fraud in 2014* Banking $32Byearly e-commerce fraud** Fraud in E-commerce is estimated to cost over 32 billion dollars annually is the US.. E-commerce The impact of fraud on the insurance industry is estimated to be $80 billion annually in the US. Insurance $80Bestimated yearly impact*** *) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc **) E-commerce expert Andreas Thim, Klarna, 2015 ***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA
  11. 11. Who Are Today’s Fraudsters?
  12. 12. Organized in groups Synthetic Identities Stolen Identities Who Are Today’s Fraudsters? Hijacked Devices
  13. 13. Types of Fraud • Credit Card Fraud • Rogue Merchants • Fraud Rings • Insurance Fraud • eCommerce Fraud • Fraud we don’t know about yet…
  14. 14. Digitized and Analog World of Fraud Constantly Evolving Few and Many Players “One Step Ahead” Simple and Complex
  15. 15. Fraud Detection (From a data-modeling perspective)
  16. 16. Raw Data
  17. 17. Anomalies
  18. 18. Patterns
  19. 19. Patterns
  20. 20. 1) Detect 2) Respond Fraud Prevention is About Reacting to Patterns (And doing it fast!)
  21. 21. Relational Database Choosing Underlying Technology
  22. 22. Data Modelled as a Graph! Graph Database
  23. 23. “Don’t consider traditional technology adequate to keep up with criminal trends” Market Guide for Online Fraud Detection, April 27, 2015
  24. 24. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Traditional Fraud Detection Methods
  25. 25. Unable to detect • Fraud rings • Fake IP-adresses • Hijacked devices • Synthetic Identities • Stolen Identities • And more… Weaknesses DISCRETE ANALYSIS Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. Traditional Fraud Detection Methods
  26. 26. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection With Discrete Analysis
  27. 27. Revolving Debt Number of Accounts Normal behavior Fraud Detection With Connected Analysis Fraudulent pattern
  28. 28. CONNECTED ANALYSIS Augmented Fraud Detection Endpoint-Centric Analysis of users and their end-points Navigation Centric Analysis of navigation behavior and suspect patterns Account-Centric Analysis of anomaly behavior by channel DISCRETE ANALYSIS 1. 2. 3. Cross Channel Analysis of anomaly behavior correlated across channels 4. Entity Linking Analysis of relationships to detect organized crime and collusion 5.
  29. 29. Preventing Fraud Networks of People Processes and Transactions Ownership E.g. e-commerce Fraud, AML E.g. detecting fraud rings, finding connections and shortest paths E.g. AML, tax fraud, legal entities Data connections assist the business by identifying patterns
  30. 30. The Power of Cypher Fraud Ring: MATCH ring = (suspect:AccountHolder)-[*]->(contactInformation)<-[*..5]-(:AccountHolder)-[*]->(suspect) RETURN ring
  31. 31. Top Tier Electronic Payment Services Case studyApply to AML regulations Challenge • Needed to apply to AML regulation • Unability to provide reports out of RDBMS leading systems Transactions fragmented and transfered „from rings to rings“ • Neo4j is used to store and report on transaction over previous 24 months • Business Users / Fraud Analysts are enabled to investigate data and detect patters Use of Neo4j • Complies to Regulations • Neo4j also enabled the company to detect potential AML usage early and act against them “We have been unable to detect AML fraud patterns in the SQL based operational systems. Graphs and Graph visualisation is a key enabler technology.” – Top Tier Payment Service Result/Outcome
  32. 32. What about Machine Learning?
  33. 33. What about Machine Learning? Neo4j is an enabler technology: • Automized detection of Fraud patterns via Cypher • Detecting Paths • Graph Algorithms (eg Centrality, Community) • Algorithms as background tasks -> mark corresponding nodes • Automatically cancel Business Transactions • Score identified patterns and weigh • ….
  34. 34. Why Graph is Superior for Fraud DetectionFraud Requirement Traditional Approaches Neo4j Approach Find connected data patterns over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time acting on incoming events in ever changing formats for potential fraud Limitations inherited from SQL Database Schema Schema free database enables to connect any nodes with each other Effort required to add new data and systems Days to weeks to rewrite schema and queries Draw new data connections on the spot Time to deployment Months to years Weeks to months Response time to Fraud requests Minutes to hours per query Milliseconds per query Form of Fraud Incidents / Investigations Text reports that are not visual and prove very little Visuals patterns and the path to follow through your system Bottom line Long, ineffective and expensive Easy, fast and affordable
  35. 35. How Neo4j fits into your environment
  36. 36. Money Transferring Purchases Bank Services Relational database Develop Patterns Data Science-team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
  37. 37. Money Transferring Purchases Bank Services Relational database Data Lake + Good for Map Reduce + Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
  38. 38. Money Transferring Purchases Bank Services Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
  39. 39. Money Transferring Purchases Bank Services Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data Data-set used to explore new insights
  40. 40. Example Fraud Solution Architecture
  41. 41. Neo4j Database Cluster Data Visualization Neo4j APOC Fraud Detection Algorithms Management Dashboard Neo4j Bolt Driver Data Ingest Mgmt. … Customer Data Sources / Systems / Applications Legend: Neo4j Provided Components Custom built Neo4j/Customer Customer/SI Fraud Reports Real Time Alerts Batch Data Buffering (Queue) Real-Time Neo4j BrowserAdmin UI UI for Fraud Analysis System Specific Adapters / Scripts / Connecters Fraud Analysts Admin / SuperuserFraud Analysts Fraud Analysts
  42. 42. Neo4j powered Fraud Solution Characteristic Benefit for Fraud Solution • Agility • Constant catch up with fraudster techniques supported • Enabled for Future Requirements • Solution can be built iteratively • Fast implementation cycles • Schema free DB supports “connect anything” • Intuitiveness • Enable Fraud Analysts to use Technology • Using visualization to detect pattern • Drilling into suspicious patterns • Speed • Unlimited number of traversals to detect complex connections within the data • Response time enables fraud prevention • Leverage Data Connections • 360 degree customer view enabled / provided • Scalability • Hardware efficiency with real-time patterns • TCO/ROI • Adding on top of existing infrastructure protects investments
  43. 43. Conclusion (graphs)-[:ARE]-> (everywhere) and (Solutions)-[:NEED]-> (graphs)
  44. 44. Who can help? Neo4j Platform Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists 3rd Party Tools “The Graph Advantage” Domain know-how Professional Services PS Packages Graph Based Solution Professional Services: - Extend and leverage Domain Expertise - Best Practices - Using Building Blocks - Don’t “re-invent the wheel” - Speed up development and deployment - Access to Neo4j infrastructure (Development, Support, Product management)
  45. 45. How to build Next Generation Solutions with Neo4j Stefan Kolmar, VP Field Engineering May 2018

×