Predictive Machine Learning
Production-oriented prototypeFraud and Anomaly Detection
Problem
Suspicious transactions are rare and shifting, so rule-based monitoring misses new fraud patterns.
System
A transaction-monitoring workflow that preprocesses transaction data, engineers behavioural features, and combines supervised ensemble models with anomaly detection under strong class imbalance. Scoring is exposed through an inference API for integration into application workflows.
My contribution
Built the transaction preprocessing and feature-engineering pipeline, trained and evaluated the ensemble and anomaly-detection models, and implemented the inference API.
System architecture
- Transaction data
- Preprocessing
- Feature engineering
- Ensemble and anomaly models
- Risk score
- Inference API
Technical focus
- Feature engineering
- Ensemble learning
- Anomaly detection
- Imbalanced data handling
- Inference APIs
Technology stack
- Scikit-learn
- XGBoost
- Pandas
- FastAPI
Data source
Dataset and data-collection write-up is being prepared.
Data pipeline
Ingestion and preprocessing write-up is being prepared.
Model or LLM workflow
Model and workflow write-up is being prepared.
Evaluation and results
Evaluation setup and results write-up is being prepared.
Challenges and engineering decisions
Key engineering decisions write-up is being prepared.