AI Solutions for Financial Fraud Detection
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Open Data Science Conference (ODSC) Talk Submission. San Francisco, CA 2017
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AI Solutions for Financial Fraud Detection
- 1. 1 © 2015 Teradata Applications and Approaches AI Solutions for Financial Fraud Chanchal Chatterjee Teradata Version 1.0
- 2. 2 Industries & Governments Facing Unprecedented Threats & Risks Hacktivists Criminal Organizations Terrorists Insiders Nation States Threat Actors Risks Intellectual Property Theft or Destruction Monetary Losses Operational Disruptions Loss of Confidential Information Reputational Damage Financial Crime Growth National Security at Risk Loss of Public Confidence © 2017 Teradata
- 3. 3 • E-Banking & E-Business • Credit Card fraud • Anti-money laundering • Predicting credit rating • Check and Document processing • User & voice recognition • ATM image recognition • High Frequency Trading Banking Fraud AI Top Use Cases © 2017 Teradata
- 4. 4 Fraud Types Customer initiated Fake invoice CEO fraud B A N K Beneficiary account change Nigeria/ investment scam Rental scam/ Goods not received © 2017 Teradata Fraudster initiated Malware @ SPEAR phishing Vishing/ support scam Phishing/ smishing ID theft Customer initiated Fraudster initiated
- 5. 5 © 2017 Teradata Current Fraud Prevention Lifecycle Group policy Dynamic Rules Withhold Payment Fraud Payments Payment Characteristics Rule Characteristics • Difficult to Update • Slow Verification • Low Detection Rate • High False Positives
- 6. 6 Our Differentiated Approach to Financial Fraud © 2017 Teradata
- 7. 7 © 2017 Teradata How Do We Differentiate? • Use cross-product, cross-channel as well as non banking data • Tight integration of AI solutions with real-time and repository data • Transformation of data into relevant features that enhance detection • Enable new & augmented business rules/policies for fraud detection • Create better understanding and insight of fraud events • Create greater trust by expanding human domain knowledge • Obtain much better detection rate due to new models and data • Increase detection at much lower false positives Data Process Performance
- 8. 8 © 2017 Teradata Data for Fraud Detection Advertising Databases Click Stream Social Media App & Web server Logs Marketing Data Order Management Customer Accounts Batch Data ERP CRM Repository Data Real-time Data Transactions Branches ATMs CDMs Internet Mobile Call Centers Relationship Mgrs/Agents POS Systems Channels SMS & Fax Kiosks MessageBus Feature Enricher Persistent NoSQL DB eBanking Credit Cards Checks Products Event Streams OLAP Datamarts EDW / MDW
- 9. 9 © 2017 Teradata Process Differentiation • Increase Understanding – Provide insight into important variables and their trends around fraud events. Increase Understanding and Trust of Fraud Events Features Leading to Fraud Fraud Filter Activations Leading to Fraud Non-Fraud Fraud NoEvent • Increase Trust • Fraud events and trends conform to human domain knowledge and expectations. • Determine that fraud explanations remain stable and in line with human domain knowledge.
- 10. 10 © 2017 Teradata Process Differentiation Augment Existing Rule Engine. Create New Business Rules and Policies • Augment Existing Rules – Recommend local factors leading to events – Recommend increments to existing process to prevent events Scaled Importance Event Incidence Root Cause • Create New Processes & Policies • Overlay events on customer journeys to recommend changes in the processes • Identify trigger points in the customer journey leading to the detected events
- 11. 11 Dramatically Improve Fraud Detection at Low False Positives © 2017 Teradata Our Deep Learning Classic Machine Learning False Positive Rates Our Deep Learning vs Machine Learning at 1% FP 44% Gain TruePositiveRates
- 12. 12 Dramatically Improve Fraud Detection over Human Rule Engine © 2017 Teradata Our Deep Learning Rules Engine False Positive Rates Our Deep Learning vs Rule Engine at 9% FP 40% Gain TruePositiveRates
- 13. 13 Fraud Detection on Public Data © 2017 Teradata Our Deep Learning vs Machine Learning at 1% FP 20% Gain
- 14. 14 Dramatically Improve Credit Risk Assessment © 2017 Teradata 35%Gain
- 15. 15 Our Approach to End to End Fraud Solution © 2017 Teradata
- 16. 16 © 2017 Teradata Our Approach: Banking Fraud Prevention at All Levels Transaction Level Account Level Network Level • State of the art Deep Learning Neural Networks and Statistical Machine Learning Models • Identify several fraud types at very low latency • Monitor account level activity to identify fraudulent patterns • Numerous fraudulent behaviors identified by state of the art deep learning algorithms • Monitor account to account interactions & across products • Many network level fraud types identified such as frequent transfers from several accounts to one account
- 17. 17 © 2017 Teradata Our Fraud Solution Capabilities Accurate detection with Low False Positives. Cross-Channel, Cross Product Detection Proactive vs Reactive Risk and Compliance • Dramatically improve fraud detection by more than 95% • Reduce false investigations to under 10% of all transactions • Generate rules in seconds, and Perform verification in milliseconds. • Fully understand entity behavior and track activity across channels with comprehensive profiling capabilities. • Contributes to dramatic improvement in fraud detection. • Deeper KYC due to deeper understanding of customer across channels and products. • Deeper risk prediction due to unusual behavior among transacting parties, auto linkages and bad actors. Transaction, Account, and Network Level Profiling • Deep Learning models apply to each transaction in real-time • Account level activity include frequent payments and suspicious profile changes • Intra account activity such as frequent transfers from several accounts to one account Adaptive, Self- Calibrating Outlier Models • Retaining benefits of the consortium models, adaptive models quickly learn recent fraud activity to sharpen detection • Adjust to changes in segment behavior or where data does not exist, identify outliers customers Capability Benefits
- 18. 18 © 2017 Teradata From Data to Detection Advertising Databases Click Stream Social Media App Logs Marketing Data Order Management Customer Accounts Batch Data ERP CRM Repository Data Real-time Data Transactions Branches ATMs CDMs Internet Mobile Call Centers Relationship Mgrs/Agents POS Systems Channels SMS & Fax Kiosks MessageBus Feature Enricher Features ScoreScore Publisher BI Publisher Score BI Reporting Visualization RULE ENGINE AI INFERENC E PLATFORM Persistent NoSQL DB eBanking Credit Cards Checks Products Event Stream OLAP Datamarts EDW MDW AI TRAINING PLATFORM Features Models
- 19. 19 © 2017 Teradata AI Platform Capabilities • Low Latency Model Serving • Both GPU and CPU Inference • Model Interpretation • Optimized GPU Utilization • Multi-GPU Multi-Node Training • Model Management • Model Tuning • Model Visualization • Increase understanding & trust of fraud events • Augment existing rules • Create new rules & policies Inference Training Interpretation Scaled Importance Fraud Incidence Root Cause
- 20. 20 © 2017 Teradata AI Inference Platform Priority Queues Kubernetes, Docker Score CPU SchedulerGPU Scheduler Teradata IntelliCloud™ Deploy Score API Serve r Features Scoring Request GPU0 Model v1 Model vN … Model Repository Scan and Load Models Teradata Analytics Platform Fraud Client Model Server GPU1 Model Server CPU0 Model Server CPU1 Model Server Score Scoring Request GPU Cluster CPU Cluster
- 21. 21 • Machine (Wide) Learning uses fundamental statistical techniques; used for memorization + • Deep Learning uses neural networks which emulate the brain; used for generalization AI Platform Training: Wide + Deep Learning Source: Google TensorFlow Dev Summit – February 2017 Combining the two achieves better performance
- 22. 22 © 2017 Teradata AI Platform Training: Configurable Learning Approach REGRESSION & BOOSTED TREES GENERATIVE ADVERSARIAL NETWORKS RECURRENT NEURAL NETWORKS CONVOLUTIONAL NEURAL NETWORKS
- 23. 23 Teradata Affinity Teradata Analytics Platform Teradata IntelliCloud™ Teradata Aster® Analytics Easy to integrate, faster access, no effort by the customer, better performance due to proprietary knowledge, faster deployments Flexible differentiated managed hosting service and support. Traditional on-premises, and multiple cloud-based managed options. Wide variety of recommender functions: Time Series, Clustering, Text Analysis, Bayesian, Ensemble, Associations, Graph, Neural Network Teradata UDA™ and QueryGrid™ Minimize data movement and process data where it resides Fast and easy bidirectional data movement to and from AI solutions and Teradata products
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