paysim

Fraud and Anomaly Detection using Synthetic Transactional Data

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.

💳 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

AI-powered fraud detection web application using Machine Learning, Flask, and Random Forest to assess transaction risk in real time.

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

A machine learning project for detecting fraudulent mobile payments using the PaySim dataset, comparing Logistic Regression, Random Forest, and XGBoost with reproducible evaluation and an interactive Streamlit demo.

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

Real-time transaction scoring: unsupervised fraud detectors on PaySim evaluated by precision, recall and lift, with a streaming path on Pub/Sub, Cloud Run and BigQuery as the next stage

Deteccion de fraude en transacciones bancarias moviles: XGBoost supervisado + Isolation Forest/LOF no supervisado, sobre PaySim