Fraud Detection System
A machine learning workflow for identifying potentially fraudulent transactions, with emphasis on evaluation methods appropriate for imbalanced datasets.
Project Summary
- Status
- Completed
- Timeline
- Completed project
- Visual proof
- Transactions -> Risk Score -> Threshold Decision
Placeholder visual
Featured case-study visual
Placeholder visual: precision-recall curve with threshold metric cards.
TODO: Replace this block with a real screenshot, chart, map, or diagram.
Problem
Fraud detection datasets are highly imbalanced, so a useful model must be evaluated through precision, recall, threshold behavior, and decision tradeoffs.
Approach
Clean transaction data, engineer features, train classification models, evaluate probability scores, and analyze how threshold changes affect business tradeoffs.
Architecture
System flow and processing stages.
Stage 1
Transaction dataset
Stage 2
Cleaning and feature engineering
Stage 3
Train/test split
Stage 4
Classification models
Stage 5
Probability scores
Stage 6
Threshold tuning
Stage 7
Fraud risk decisions
Data Sources
- Public fraud transaction dataset
Methods
- Classification
- Feature engineering
- Imbalanced data evaluation
- Precision-recall analysis
- Threshold tuning
Technologies
- Python
- pandas
- scikit-learn
- matplotlib
- seaborn
Evidence and Screenshots
Visual assets to replace placeholders.
Placeholder visual
Precision-recall curve
Create this screenshot and replace this placeholder in the project assets.
TODO: Replace this block with a real screenshot, chart, map, or diagram.
Placeholder visual
Threshold simulator dashboard
Create this screenshot and replace this placeholder in the project assets.
TODO: Replace this block with a real screenshot, chart, map, or diagram.
Placeholder visual
Feature importance or SHAP chart
Create this screenshot and replace this placeholder in the project assets.
TODO: Replace this block with a real screenshot, chart, map, or diagram.
Results
- Built an end-to-end fraud classification workflow.
- Evaluated model behavior using metrics suited to imbalanced data.
- Framed fraud detection as a decision problem rather than only a prediction problem.
Metrics and Evaluation Needed
- Precision-recall curve
- Confusion matrix by threshold
- Class imbalance chart
- Feature importance
Challenges
- Class imbalance makes accuracy misleading.
- False positives and false negatives have different costs.
- Threshold choice changes model usefulness.
Lessons Learned
- Evaluation metrics should match the business decision.
- Precision-recall curves are more useful than accuracy for rare event detection.
- Interactive threshold exploration makes model tradeoffs easier to understand.
Future Work
- Add an interactive threshold simulator.
- Add SHAP or feature importance explanations.
- Add cost-sensitive evaluation.
Interactive Demo Ideas
- Threshold tuning simulator
- Cost tradeoff calculator
What This Demonstrates