SMART FRAUD DETECTION FRAMEWORK USING DEEP LEARNING
Keywords:
Deep Learning, Fraud Detection, LSTM Networks, Online Banking Security, Anomaly Detection, Real-Time Analytics.Abstract
A Smart Fraud Detection Framework applying deep learning is introduced to significantly improve security in digital finance. The model utilizes advanced sequential neural networks, specifically Long Short-Term Memory (LSTM) architectures, to analyze complex temporal patterns within transaction sequences. This enables the autonomous learning of nuanced user behavior and the detection of sophisticated, non-linear fraud signatures that elude conventional methods. Engineered for real-time operation, the system integrates with streaming data infrastructure to provide instantaneous risk scoring for each transaction, allowing for immediate preventative action. By proactively identifying evolving fraudulent schemes with high precision, the framework aims to drastically reduce financial losses and false positives, thereby enhancing operational efficiency and fostering greater trust in online banking ecosystems.
