An optimal method using machine learning algorithms to detect fraud in banking services

Abstract

In contemporary times, a substantial number of financial transactions and monetary transfers take place on the Internet and within electronic environments, thereby incentivizing fraudsters to infiltrate this domain. Consequently, the identification of individuals' identities in electronic service provision is exceedingly vital and crucial. This article aims to fraud detection in the banking system and present an optimal method utilizing artificial intelligence tools and model evaluation on the bank information of the Development and Cooperation Cooperative. In the initial phase, a gradient boosting algorithm, chosen for its high computational speed, is employed to train on a set of input data to identify and classify patterns of suspicious behaviors. In the second phase, an algorithm based on gradient boosting is utilized to refine results and optimize accuracy. To evaluate this approach, real data from a bank is employed, and the obtained results demonstrate that this method significantly enhances the speed and accuracy of fraud detection.