Quant Model
Fraud Risk Analyzer — Bayesian Network
Interactive Bayesian Belief Network for transaction fraud detection. Models probabilistic relationships between transaction attributes to calculate fraud risk scores with explainable factor contributions.
A Bayesian Belief Network implementation for financial fraud risk scoring. Unlike black-box ML models, this approach uses probabilistic graphical modeling to capture domain knowledge about fraud indicators and their interdependencies.
What It Models
The network reasons about:
- Account Age — New accounts carry higher baseline risk
- Past Fraud History — Previous fraud is highly predictive
- Transaction Amount — High-value transactions warrant scrutiny
- Time of Day — Late-night activity correlates with fraud
- Location — International transactions increase risk
- Transaction Velocity — Rapid-fire transactions signal automation
Technical Approach
- Bayesian Inference — Updates prior fraud beliefs with transaction evidence
- Explainable Scoring — Shows which factors drive the risk assessment
- Scenario Testing — Quick presets for common fraud patterns:
- Routine Purchase (Low Risk ~2%)
- Suspicious International (High Risk ~65%)
- Late Night High-Value (Medium-High ~45%)
- New Account Velocity Spike (Very High ~85%)
Why Bayesian Networks for Fraud
Traditional ML models can achieve high accuracy but lack transparency. Bayesian networks explicitly model causal relationships and provide:
- Clear factor contribution breakdowns
- Natural handling of missing evidence
- Domain knowledge integration
- Uncertainty quantification
This project demonstrates the quant analyst skill of building interpretable risk models for high-stakes decisions.