Proactive Fraud Detection

Repository: Fraud Detection Analysis Notebook: fraud_detect.ipynb Author (notebook): Vyom

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Project summary

This project contains an end‑to‑end exploratory analysis and machine learning pipeline for detecting fraudulent transactions using the provided Fraud.csv dataset. The notebook walks through data loading, cleaning, feature engineering, imbalance handling, model training and hyperparameter tuning, model interpretation (feature importance + SHAP), and business‑facing insights and recommendations.

The goal is to provide a production‑ready analysis that data science, ML engineering, and risk teams can use to move toward a real‑time scoring pipeline.

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Table of contents

1. Project overview
2. Dataset
3. Key notebook sections
4. Prerequisites / environment
5. How to run
6. Expected outputs and artifacts
7. Modeling details
8. Evaluation & interpretation
9. Productionization checklist
10. Limitations & future work
11. Contact

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1) Project overview

• Type: Binary classification (fraud vs non‑fraud)
• Primary model: XGBoost (tuned via GridSearchCV) — identified as the best performing model in the notebook
• Imbalance strategy: SMOTE (oversampling of minority class)
• Interpretability: Feature importance (XGBoost) + SHAP values for local/global explanations
• Preprocessing highlights: IQR outlier clipping, log transforms of monetary fields, engineered balance deltas & ratios, time features (hour/day), one‑hot encoding for transaction type

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2) Dataset

• File expected: Fraud.csv (place in the same folder as the notebook when running)
• Primary fields used: step (time), type (transaction type), amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest, isFraud (target) and other system/flag fields present in the CSV
• Note: The notebook derives additional columns such as hour, day, log‑transformed monetary features, origin_balance_delta, dest_balance_delta, and ratio features.

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3) Key notebook sections (high level)

• 1. Imports & environment setup — packages and helper functions.
• 2. Data loading & basic EDA — shape, missing values, distribution of target by merchant/transaction type.
• 3. Outlier handling — IQR clipping (implemented via handle_outliers) and before/after visuals.
• 4. Multicollinearity check — VIF calculation and correlation heatmap for monetary features.
• 5. Feature engineering — log transforms, deltas, ratios, time features, and encoding transaction type.
• 6. Train/test split & scaling — StandardScaler used for numeric features.
• 7. Class imbalance — SMOTE oversampling on training data.
• 8. Model training & tuning — baseline models and GridSearchCV to tune XGBoost hyperparameters.
• 9. Model evaluation — accuracy, precision, recall, F1, ROC AUC and confusion matrix analysis.
• 10. Interpretability — feature importance plot and SHAP summary/force plots.
• 11. Business insights & recommendations — actionable items for monitoring and model deployment.

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4) Prerequisites / environment

Recommended Python environment (one option using pip):

python >= 3.8
pip install numpy pandas matplotlib seaborn scikit-learn xgboost imbalanced-learn shap statsmodels jupyterlab

Alternatively use conda to create an isolated environment.

Note: SHAP requires a C++ build toolchain for speed, but it installs on most platforms via pip as shown above.

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5) How to run

1. Place Fraud.csv in the repository root (same folder as fraud_detect.ipynb).
2. Start JupyterLab / Notebook:

jupyter lab
# or
jupyter notebook

3. Open fraud_detect.ipynb and run cells sequentially. The notebook is organized to run top‑to‑bottom.

Optional: convert the notebook to a script for automated runs:

jupyter nbconvert --to script fraud_detect.ipynb
python fraud_detect.py

(If converting to a script, ensure path variables and interactive plotting calls are handled appropriately.)

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6) Expected outputs and deliverables

• Exploratory plots (distributions, boxplots, correlation heatmap, time‑based histograms)
• Cleaned dataset preview and final feature matrix (X) and labels (y)
• Model training logs and best hyperparameters (GridSearchCV results)
• Evaluation metrics (accuracy, precision, recall, F1, ROC AUC) printed to the notebook
• Feature importance chart (XGBoost) and SHAP summary plots
• Business insights printed in the final notebook cell describing monitoring and next steps

Artifacts to save (recommended):

• best_model_xgb.joblib or best_model_xgb.pkl after training
• feature_list.json recording the final feature order used by the model
• Evaluation report (CSV / JSON) with metrics and confusion matrix

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7) Modeling details (concise)

• Feature preprocessing: IQR clipping for outliers, log(1+x) transforms for monetary fields, engineered deltas and ratios, one‑hot encoding for categorical transaction type, scaling with StandardScaler.
• Imbalance handling: SMOTE applied only to training data after train/test split.
• Models explored: Logistic Regression, Random Forest, XGBoost (notebook chooses tuned XGBoost as final candidate).
• Hyperparameter tuning: GridSearchCV on XGBoost hyperparameters such as max_depth, n_estimators, learning_rate, etc.
• Interpretability: Tree‑based feature importance and SHAP (global and per‑example) used to explain model decisions.

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8) Evaluation & interpretation

• Evaluate with multiple metrics: precision and recall are critical for fraud detection (emphasize recall and precision tradeoff depending on business cost of false positive vs false negative).
• Use ROC AUC for model ranking; however threshold tuning on predicted probabilities is necessary for production (choose threshold by maximizing business utility or via Precision‑Recall curve).
• SHAP plots provide both global feature importance and local explanations for flagged transactions — extremely useful for audit trails and compliance.

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9) Productionization checklist (next steps to convert notebook into production)

• Convert notebook into modular Python scripts or a package with clear interfaces (preprocessing, predict, explain).
• Persist preprocessing pipeline (scaler, feature list, encoders) and model with joblib or pickle.
• Expose a prediction API (FastAPI or Flask) that accepts raw transaction payloads and returns prediction + top SHAP contributors.
• Implement streaming or micro‑batch scoring (Kafka/Flask + Redis) for near‑real‑time detection.
• Monitoring: model drift, data drift (via PSI), alerting on spike in predicted fraud rates and false positives.
• Logging and audit trail: store input features, model scores, SHAP values and final decision for each flagged transaction.
• Retraining strategy: scheduled retrain (weekly/monthly) or trigger‑based retrain when drift exceeds thresholds.

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10) Limitations & suggested improvements

• Data limitations: training distribution may not represent future fraud patterns. Maintain continuous labeling and feedback loop.
• Feature scope: consider adding entity enrichment (customer profiles, device fingerprinting, IP geolocation, transaction velocity features).
• Imbalance & cost sensitivity: SMOTE helps, but consider ensemble strategies, cost‑sensitive learning or anomaly detection models for rare fraud types.
• Explainability at scale: SHAP is powerful but computationally heavy; investigate faster approximations or limit SHAP to flagged transactions only.

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11) Contact / Author

Notebook author: Vyom (as included in the notebook header)

For changes, questions or production support requests, leave a short issue or reach out to the data science team owning this notebook.

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End of README