Abstract
The exponential growth of online transactions has increased the vulnerability of fraudulent activities in payment systems and necessitated sophisticated and smart detection techniques for real-time fraud protection. Conventional machine learning techniques tend to perform poorly with high-dimensional data, skewed classes, and evolving fraud patterns, which affect detection accuracy. In order to overcome these shortcomings, the study presents a new model, which is Coupled Modular Simplicial Graph Neural Network with Snow Ablation Optimization (CMSGNN-SAO) to effectively detect fraud in real-time. The process starts with pre-processing, where raw data from the Credit Card Fraud Detection (CCFD) dataset are processed using the Adaptive Morphological Wavelet Perona-Malik (AMWPM) filtering algorithm to remove noise, normalize features, and maintain data quality. Next, Feature…