Product · 6 documents
On a 30,000-event synthetic stream with a 2.40% overall fraud rate, the proposed dual-horizon FPAA model achieves an average precision of 0.427 ± 0.038 across three training seeds.
The study develops a scalable MLOps framework evaluated on 100,000 transactions sampled from the Credit Card Transactions Fraud Detection Dataset.
UPI Fraud Shield is a prototype designed to monitor digital payment fraud in real time using tabular learning, graph neural networks, explainable AI, and an early-warning layer.
The framework addresses extreme class imbalance in a dataset comprising 6,362,620 transaction records, where only 8,213 records (0.1291%) represent fraudulent activity.
The proposed machine learning fraud detection pipeline achieves an ROC-AUC of 0.984 and a PR-AUC of 0.886 across 284,807 European cardholder logs.
On a six-million-transaction synthetic dataset, the proposed ensemble attains a recall of 0.9184 at a false-positive rate of 0.0186.
