Leveraging ML to Create a Reliable Anti-Fraud System
summary
Claims a working, production anti-fraud pipeline built over four years and deployed in Yandex advertisement that detects payment and gaming cheats by modeling a payment-cost time series and segmenting by country. Uses gradient boosting and AO regression with ensemble fallback, keeps models and events in a Data Lake and Kafka flow, and relies on C++ market-quotations services and Trin/GPU options for heavy compute. Argues that neural networks underperform in rapidly adversarial fraud and that automated blocking plus human review of borderline cases actually reduces loss.
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chapters (10)
- 0:00Introduction to Anti-Fraud
- 1:09Understanding Fraud and Anti-Fraud Mechanisms
- 2:24Fraud in Mobile Gaming
- 3:18Payment Fraud in FinTech
- 4:34Building an Automatic Anti-Fraud System
- 5:41Challenges with Machine Learning in Anti-Fraud
- 7:50Infrastructure and Data Considerations
- 8:30Ensemble Models and Risk Management
- 11:39Operational Workflow and Monitoring
- 15:24Conclusion and Key Takeaways
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