Fraud detection with machine learning: uncovering hidden transaction patterns

1Introduction

Static rules are quickly bypassed and generate false positives.

2Discussion

ML scores each transaction using amount, time, location, device and past behaviour; unsupervised methods catch new patterns.

Class imbalance demands balanced sampling and precision/recall metrics rather than accuracy.

3Conclusion

Best results combine models, business rules and analyst review in a feedback loop.