Machine learning workflow for suspicious activity signals
Fraud Detection ML Project
An applied ML project focused on identifying suspicious transaction patterns and turning model output into reviewable product signals.
- Python
- Pandas
- Scikit-learn
- ML evaluation
- Data analysis
Problem
Fraud signals are hard to use when they live as raw model scores without context, workflow, or explainability.
Goal
Explore a practical fraud detection pipeline that supports review, prioritization, and clearer decision-making.
My role
Data/product engineer shaping the ML workflow, evaluation framing, and product interpretation.
What this proves
Python data workflow, ML evaluation, risk-signal thinking, and product interpretation.
Architecture
- Data preparation layer
- Feature engineering and model training
- Evaluation/reporting step
- Product-facing risk signal concept
Features
- Feature exploration
- Suspicious-pattern classification
- Model evaluation framing
- Review-oriented output concepts
Technical decisions
Challenge
Making predictions interpretable enough for review
Decision
Framed outputs around user review needs
Challenge
Avoiding a model-only project story
Decision
Connected model performance to decision workflows
Challenge
Thinking through false positives and operational trust
Decision
Documented learning and tradeoffs instead of hiding uncertainty
Results and learning
Shows that I can frame an ML project around review workflows, trust, and decision-making instead of only model output.
AI projects are strongest when the interface and review workflow are part of the system design.