🛡️ Fraudulent Transaction Detection System

A full-fledged fraud detection pipeline using machine learning and AI interpretability tools. This project includes data exploration, feature engineering, model selection (with XGBoost), visual explanations (SHAP), a Flask API, and a Streamlit frontend. We also integrated Google’s Gemini AI for advanced prediction interpretation.

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📊 1. Dataset & Exploratory Data Analysis

• Source: synthetic_fraud_dataset.csv
• We conducted Exploratory Data Analysis (EDA) using ydata-profiling and custom visualizations to understand:
  • Feature distributions
  • Correlation heatmaps
  • Class imbalance
  • Potential data quality issues

The EDA helped inform downstream preprocessing and feature engineering.

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🧠 2. Feature Engineering

We selected and transformed features to improve model accuracy and reduce noise.

✅ Important Features:

• Account_Balance, IP_Address_Flag, Previous_Fraudulent_Activity
• Daily_Transaction_Count, Risk_Score, Is_Weekend, Hour, Amount_Deviation, etc.

❌ Dropped/Transformed Features:

• Identifiers like Transaction_ID, User_ID
• Raw Timestamp, Device_Type, Location (transformed into derived features)
• Low-informative fields like Card_Type, Authentication_Method

We scaled and normalized the final dataset, saving it as preprocessed_fraud_dataset_numerical_only.csv.

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🏗️ 3. Model Selection & Training

We tested multiple classifiers:

• ✅ Logistic Regression
• ✅ Random Forest
• ✅ Gradient Boosting
• ✅ XGBoost (Best Performance)

After selecting XGBoost, we fine-tuned hyperparameters and saved the trained pipeline to fraud_detection_model_numerical.pkl.

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📈 4. Model Validation & Interpretation

We built utility functions to:

• Predict fraud likelihood on new transactions
• Show evaluation metrics:
  • Confusion Matrix
  • Precision-Recall Curve
  • Classification Report
  • ROC-AUC Score
• Visualize feature importances and interpret predictions with SHAP:

=== TRANSACTION DETAILS ===
Transaction_Amount: 1500
...
Amount_Deviation: 1300

=== PREDICTION ===
Prediction: FRAUD
Fraud Probability: 0.82

Visuals include:

• SHAP summary bar plot
• Force plots for individual transaction explanations

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🧪 5. Flask API

We exposed the model via a REST API using Flask.

🔌 API Endpoints:

Method	Endpoint	Description
POST	/predict	Predict using XGBoost model
POST	/ai-prediction	Get prediction + explanation via Gemini
GET	/health	API health check

Example AI Prompt (Gemini):

"You are an AI fraud detection assistant. Based on the following transaction data..."

Uses gemini-2.5-pro-exp-03-25 for rich explanations alongside predictions.

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💻 6. Streamlit Frontend

We developed a simple, responsive Streamlit app to interact with the API:

• Enter transaction details manually
• Get instant predictions
• View explanations (via AI or SHAP)
• Toggle light/dark theme

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🚀 Running the Project

▶️ Run Combined Flask + Streamlit

bash app/start.sh

📦 Manual Steps (Optional)

1. Start the Flask API:

python app/api.py

2. Launch the Streamlit UI:

streamlit run app/frontend.py

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📌 Technologies Used

• Python (Pandas, Scikit-learn, XGBoost, SHAP)
• Flask for backend API
• Streamlit for UI
• Google Gemini AI for natural language explanations
• Matplotlib & Seaborn for visualization
• Joblib for model serialization

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📍 Future Improvements

• Add authentication & rate limiting to API
• Integrate live transaction feeds for real-time fraud scoring
• Add model monitoring dashboard (e.g., with Grafana + Prometheus)
• Expand AI explanation options using multiple LLMs

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🤝 Contribution

Feel free to open issues, fork the repo, or create pull requests. Your feedback and ideas are welcome!

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📜 License

MIT License © 2025

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