Integrating Deep Learning and MIS for Fraud Detection in Financial Systems

Fraud detection in financial transactions is a serious challenge due to the increasing sophistication of fraud tactics and the looming cyber threat. This research proposes a novel methodology fusing Deep Learning and Management Information Systems (MIS) for fraud detection in payment systems. The proposed method balances precision and recall by utilizing graph neural networks (GNNs) for transaction analysis and autoencoders to detect anomalies. Such scalability and realtime processing are essential for large-scale streaming and batch data processing with a Lambda architecture [1]. Including the MIS provides sector-related contextual understanding to support action and decision-making. Experiments conducted on real-world bank datasets show that the proposed methods achieve higher fraud detection rates at lower false favorable rates. The “neural” model also allows for proactive risk management by revealing emergence fraud patterns. This research contributes by considering the revolutionary effects of deep Learning [2] and MIS convergence [3] on the banking and finance sectors to improve security and performance.