Online Payment Fraud Detection using Logistic Regression, Decision Tree, Random Forest & Gradient Boosting Models

ML-based online payment fraud detection system using multiple models and ensemble learning.

Led development of an online payment fraud detection system using optimized XGBoost, achieving 99.9% AUC. Managed full ML pipeline from data ingestion to feature engineering and EDA, compared multiple models, and processed 6.3M+ transactions to build a scalable, high-precision detection system.