Fraud Prevention, Machine Learning, and Design Patterns: Keep Your Analysts in the Loop
summary
Designing antifraud systems that keep human analysts in the loop by combining machine learning, software engineering, and expert review. It presents a human-in-the-loop (HIL) design pattern using risk scoring to auto-approve low-risk cases, apply stricter measures for high-risk events, and route ambiguous cases for analyst review while feeding analyst feedback back into rules and predictive models; practical trade-offs like data quality, latency, governance, and compliance are discussed.
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chapters (12)
- 0:00Introduction
- 1:23Fraud as architecture problem
- 6:42Daily fraud challenges
- 9:10Anti-fraud system components
- 12:22Software architecture in fraud
- 14:49Design patterns in systems
- 16:33ML enables human in triage
- 21:46Design pattern anatomy
- 23:52Blueprint: Design phase
- 24:12Blueprint: Operate phase
- 24:34Summary
- 25:16Q&A
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