A Comprehensive Framework for Analyzing AI-Driven Talent Development Systems in the Modern Banking Sector using Interactive Data Visualization
Abstract
The issue of theft using credit cards has become one of the challenges in the contemporary commercial environment as a result of the increase in cashless and electronic transactions involving customers and banks. The growing practice of online shopping, mobile banking, and e-payment has resulted in millions of transactions daily. The issue of detecting fraud is no longer an important aspect of technology, but a significant feature to be considered in the development and sustenance of trust and security in digital economies. However, traditional fraud detection methods have been centered on the use of archaic rule-based approaches or those that lack flexibility with regard to dynamically applied fraud, data class imbalance, and rapid volume of modern transactions. These are some of the challenges that highlight the importance of having intelligent and perfect fraud detection techniques. The main objective of this research paper is to suggest some possible solutions through the use of ML and DL algorithms, which can help the system deal with the shortcomings associated with conventional methods of fraud detection. The problem of fraud detection in credit card transactions is thoroughly analyzed, taking into account such issues as data imbalance, need for real-time decisions, and constantly updated strategies employed by fraudsters. The literature analysis reveals that there exist some problems connected with the accuracy of predictions and computational speed of current methods used to address this issue. These difficulties provide an excellent starting point to create more effective fraud detection approaches.