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Fraud Detection & Machine Learning
Section 6 of 9
Rule engines, real-time ML feature stores, graph analysis for fraud rings, model serving, SHAP explainability, and regulatory requirements for automated decisions
3.5 hours•advanced
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Key Takeaways
- •Rule engines catch known fraud patterns instantly; ML catches unknown patterns — you need both in a layered defence
- •Feature freshness is the hardest problem in real-time ML: a feature computed 10 minutes ago is useless for detecting account takeover
- •Graph analysis (finding fraud rings) requires different data structures than tabular ML — Neo4j or Neptune for connected data
- •SHAP values are the standard explainability tool for regulators — a model that can't explain its decisions fails regulatory requirements
- •Model drift in fraud detection is fast — fraudsters adapt within days, requiring continuous retraining pipelines
📝Personal Notes
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