Developing AI-based Fraud Detection Systems for Banking and Finance

Abstract

Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems.