A Deployment-Derived Online Banking Fraud Detection Inference-Log Dataset from a Live Cloud-Based Deep Learning System

The data were gathered through a REST API endpoint hosted on a Ubuntu Linux virtual private server, where a trained hybrid CNN-LSTM model processed each incoming transaction in real time. The dataset contains 98 confirmed fraudulent transactions (0.172% fraud rate).

Each record includes 30 input features alongside model-generated outputs comprising fraud probability score, risk level, confidence estimate, action recommendation (BLOCK or ALLOW), and response latency in milliseconds. The deployment was a proof-of-concept system operating as a public demonstration interface and not a licensed banking institution.