The AI Fraud Intelligence & Risk Scoring Platform is a production-style machine learning system designed to detect suspicious financial transactions in real time using advanced fraud detection techniques.
Unlike beginner ML projects, this system demonstrates:
- end-to-end ML engineering,
- explainable AI,
- API integration,
- Dockerized deployment,
- interactive analytics dashboards,
- fraud risk scoring,
- production-oriented architecture.
This project simulates how modern fintech fraud intelligence systems operate in real-world environments.
Financial institutions lose billions of dollars annually due to:
- payment fraud,
- account takeovers,
- identity abuse,
- transaction manipulation,
- suspicious behavioral activity.
The objective of this system is to:
while minimizing false positives and maximizing fraud detection accuracy.
- XGBoost-based fraud classification pipeline
- Probability-based fraud scoring
- Configurable fraud thresholds
Each transaction receives:
- fraud probability,
- fraud prediction,
- risk level classification.
Risk levels:
- Low
- Medium
- High
- Critical
Integrated SHAP explainability to:
- explain why transactions were flagged,
- visualize feature contributions,
- improve model transparency.
Built with Streamlit:
- CSV upload support
- live fraud analysis
- KPI monitoring
- risk analytics
- feature importance visualization
- fraud explanation interface
Production-style REST API:
- real-time inference endpoint
- scalable prediction architecture
- Docker-compatible backend service
Full containerized deployment using:
- Docker
- Docker Compose
Supports:
- reproducibility,
- isolated environments,
- production-style orchestration.
┌────────────────────┐
│ Transaction CSV │
└─────────┬──────────┘
│
▼
┌────────────────────┐
│ Streamlit Dashboard│
└─────────┬──────────┘
│ API Request
▼
┌────────────────────┐
│ FastAPI API │
└─────────┬──────────┘
│
▼
┌────────────────────┐
│ XGBoost ML Pipeline│
└─────────┬──────────┘
│
▼
┌────────────────────┐
│ Fraud Predictions │
│ Risk Scoring │
│ SHAP Explanations │
└────────────────────┘
The project uses the:
This dataset contains:
- transactional features,
- behavioral patterns,
- identity information,
- high-dimensional fraud signals.
The pipeline includes:
- missing value handling,
- categorical encoding,
- preprocessing pipelines,
- feature engineering,
- scalable transformations.
Implemented:
- XGBoost classifier
- probability-based fraud scoring
Chosen because:
- excellent performance on tabular data,
- strong fraud detection capability,
- industry adoption in fintech systems.
Fraud datasets are highly imbalanced.
Handled using:
- threshold tuning,
- probability calibration,
- business-aware evaluation metrics.
This system integrates:
SHAP enables:
- transaction-level explanations,
- feature contribution visualization,
- model transparency for fraud analysts.
Example explanation:
High transaction amount
+ risky device
+ unusual behavioral pattern
→ elevated fraud probability
- Total transactions
- Fraud alerts
- Average fraud probability
- Critical-risk transaction count
- Risk distribution charts
- Fraud probability histograms
- Feature importance analysis
- SHAP waterfall plots
Download prediction results directly from dashboard.
GET /Response:
{
"message": "Fraud Detection API Running"
}POST /predictInput:
- transaction CSV file
Output:
[
{
"Fraud_Probability": 0.91,
"Predicted_Fraud": 1,
"Risk_Level": "Critical"
}
]| Category | Technology |
|---|---|
| Language | Python |
| Machine Learning | XGBoost |
| Data Processing | Pandas, NumPy |
| Explainability | SHAP |
| Backend API | FastAPI |
| Dashboard | Streamlit |
| Visualization | Plotly |
| Deployment | Docker |
| Container Orchestration | Docker Compose |
docker-compose builddocker-compose uphttp://localhost:8501
http://localhost:8000/docs
project/
│
├── api/
│ └── main.py
│
├── dashboard/
│ └── app.py
│
├── models/
│ └── xgboost_pipeline.pkl
│
├── notebooks/
│
├── data/
│
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── .dockerignore
│
├── LICENSE
│
└── README.md
The model was evaluated using:
- ROC-AUC
- Precision
- Recall
- F1-score
- PR-AUC
The system prioritizes:
This reflects real-world fraud detection business requirements.
This project demonstrates:
- ML pipeline engineering,
- API-based inference,
- explainable AI,
- Dockerized deployment,
- interactive analytics,
- scalable architecture,
- production-oriented design.
Potential enterprise extensions include:
- Kafka integration
- live transaction scoring
- autoencoders
- anomaly detection systems
- fraud ring detection
- connected suspicious entity analysis
Use GenAI systems to:
- summarize suspicious activity,
- explain fraud patterns,
- assist fraud investigators.
Unlike beginner ML projects, this system demonstrates:
- production ML engineering,
- deployment architecture,
- explainable AI,
- scalable APIs,
- real-world fraud analytics,
- fintech-oriented problem solving.
This project resembles: