Anti-Fraud in Reverse and the Use of ML Methods in It
summary
Discusses turning traditional anti-fraud systems inside out by using machine learning to detect legitimate human buying behavior rather than only blocking fraud; describes applying ML models and data features from payment flows (card entry, SMS OTP, session signals) to reduce false positives and improve conversion for online retailers. Focuses on practical implementation details and outcomes from deploying these techniques in e-commerce payment pipelines.
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chapters (14)
- 0:00Introduction
- 1:11Anti-fraud in reverse
- 3:39Bank operation lifecycle
- 5:04Miracept protocol evolution
- 7:11Fraud protection roles
- 8:13Decision service role
- 10:21Service improvement goals
- 10:41Feature space formation
- 13:32Threshold selection
- 15:20ML algorithm comparison
- 17:51Model deployment results
- 19:35System architecture
- 23:09Summary
- 24:11Q&A
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