Intelligent System for Fraud Detection in Online Banking using Improved Particle Swarm Optimization and Support Vector Machine

In the modern day, online banking has become the most popular service used by banking users. Banks collect vast amounts of useful information on their customers and their transactions every second. Financial institutions can't gain the insights they need without first securing and properly analyzing this important data utilizing big data analytic methods. The current business climate places a premium on analyzing massive data sets consisting of diverse data in order to unearth previously unseen patterns, market trends, client preferences, and other business insights. The purpose of this research is to suggest a strategy for employing IPSO-SVM to detect and prevent financial fraud in the digital sphere. This investigation introduces an improved particle swarm optimization of support vector machine (IPSO-SVM) technique model for fraud detection by combining optimized particle swarm optimization (IPSO) and support vector machine (SVM). The proposed approach outperforms other two models such as CNN and SVM.