Fraud Detection Case Study: How ML Finds Suspicious Transactions
Fraud Detection (Case Study)
Fraud is tricky because fraud cases are usually very few compared to normal cases (imbalanced data).
How I Handle It
- Features: transaction frequency, amount spikes, location mismatch
- Models: logistic regression, tree-based models
- Metrics: precision/recall (accuracy is misleading)
