Risk Prediction In Banking Transactions Utilizing A Multi-Agent Model And Deep Learning Techniques

Abstract

The banking industry encounters escalating difficulties in recognizing and mitigating risks owing to the intricacy of financial transactions and a rise in fraudulent activities. This study introduces a system that integrates many agents with deep learning to enhance risk prediction in the banking sector. Each agent concentrates on certain tasks such as data cleansing, feature selection, and anomaly detection, therefore facilitating a comprehensive risk assessment. A deep learning algorithm analyzes extensive transaction data to detect patterns that may indicate possible problems. Empirical analyses of actual banking data demonstrate that this methodology is superior in accuracy, speed, and efficacy compared to conventional techniques. This work integrates the advantages of multi-agent systems with deep learning to provide a robust and adaptable solution for banks to monitor and respond to evolving risks more efficiently.