Projects Fraud ML
Fraud ML Detection
An end-to-end fraud pipeline: ingestion, features, ranking, and decisioning with evaluation and monitoring.
Problem
Fraud is adversarial and non-stationary. The hard part is not just modeling, but building the system around the model: reliable features, evaluation you trust, and feedback loops that keep the solution effective over time.
Approach
- Event-driven ingestion that produces clean, validated inputs for feature building.
- A scoring path designed for latency and explainability where possible.
- Evaluation and monitoring that detect drift and data quality regressions early.
Highlights
Features with guardrails
Feature computation that is versioned and validated to prevent silent breakage.
Evaluation you can trust
Metrics that map to decisions: false positives, false negatives, and cost tradeoffs.
Monitoring and drift
Alerts that catch upstream changes early and make issues actionable.
What I would show in a live demo
- Ingest an event stream and compute features
- Score transactions and explain the ranking signal
- Monitoring dashboards and an example drift scenario