Generate network graphs of virtual mobile money networks including simulating some bad apples

Fraud and Anomaly Detection using Synthetic Transactional Data

Bank Fraud Detection with Imbalanced Data: Applying Oversampling and Hyperparameter Optimization

Financial Intelligence Unit (FIU) case study on the PaySim synthetic transactions dataset. Featuring SQL and Python (Pandas, NumPy, Scikit-learn, Matplotlib) workflows for anomaly detection, AML threshold analysis, and financial crime data visualization.

Enterprise-grade fraud & AML detection with ML and deep learning (XGBoost, LightGBM, Autoencoder, LSTM, Transformer). Real-time API, explainability (SHAP), BI export, Streamlit dashboard. PaySim-compatible.

SQL-based behavioural fraud detection analysis on the PaySim financial transactions dataset, identifying weaknesses in rule-based bank systems and proposing improved detection logic using window functions.

Class-weighted Global AdaBoost for rare-event fraud detection in PySpark, with threshold tuning and scalability analysis.

An XGBoost-based fraud detection modelto identify money laundering in mobile transactions using PaySim synthetic dataset.

💳 Payment Fraud Detection ML Model — XGBoost + SMOTE on 10,000 PaySim transactions (1.12% fraud rate). PR-AUC 1.00 · Zero false negatives · dest_balance_zeroed top feature (45.25%). Class imbalance handled via SMOTE. Python · XGBoost · imblearn

K-Means clustering on 6.3M financial transactions to discover hidden fraud patterns. Unsupervised model identified a high-risk cluster with 7x higher fraud rate — without any labels.

End-to-end fraud detection ML platform with calibrated modeling, cost-sensitive thresholding, risk-band decisioning, and production-style batch scoring.

Fraud detection on 6.3M financial transactions using XGBoost. Handles extreme class imbalance (0.13% fraud) with scale_pos_weight. Achieves ROC-AUC of 0.999 with SHAP explainability. Built on PaySim synthetic dataset.

ML pipeline for mobile-money fraud detection on PaySim — XGBoost + SHAP + FastAPI

Built a rule-based fraud detection system using SQL on 6.3M+ PaySim transactions. Flagged ₹12B+ fraud, extracted key business insights, and designed enterprise-level SQL risk logic for fintech fraud mitigation.

Full ML pipeline for financial fraud detection on the PaySim dataset. Features rolling-window engineering, XGBoost with class-imbalance handling (PR-AUC 0.97), business-threshold optimization, and SHAP interpretability.

Fraud classifier on 6.3M PaySim mobile-money transactions. XGBoost at 99.85% precision / 99.56% recall on a 132K-row time-based holdout, verified end-to-end by re-running the training pipeline against the real dataset.