paysim

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

Detecção de fraude em transações financeiras sintéticas (PaySim): comparação de modelos supervisionados em cenário fortemente desbalanceado, com dashboard Streamlit publicada.

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.

Large-scale PaySim fraud analytics using SQL/DuckDB, risk segmentation, BI-ready reporting, dashboards, and model-supported review prioritization.

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.

💳 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

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.

Feature store for PaySim fraud detection using Feast, with point-in-time correct training features, Parquet offline storage, SQLite online serving, and offline/online consistency checks.

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.

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

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 on mobile-money transactions — leakage-free scikit-learn pipeline, six hypothesis tests, and a 3-model comparison (Random Forest: 0.9982 F1, 0.9999 PR-AUC).

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.

Auditoria do PaySim e reconstrução sem os atalhos do simulador: uma regra de duas linhas pega 97,7% das fraudes sem modelo. Decisão modelada como triagem sob capacidade finita, ordenando a fila por valor em risco.

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

End-to-end mobile-money fraud detection using PaySim, causal feature engineering, temporal validation, XGBoost, error analysis, and a Streamlit demo.

Cost-sensitive fraud/AML detection on 6.36M real PaySim transactions - an ML systems project: time-based CV, a capacity-based operating point, SHAP, a versioned serving bundle, FastAPI live on Cloud Run, a Streamlit dashboard, and PSI drift monitoring. Runs at $0.

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.