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This research aims to develop an effective model for real-time fraud detection in payment systems.

February 17, 2026Scientific Reports0 citationsOpen Access

Coupled modular simplicial graph neural network with snow ablation optimization for real-time fraud detection in payment systems

VRVenkata Chary Sri RamojuVisvesvaraya Technological University SBSthitipragyan BiswalSymbiosis International University KKKetan KotechaSymbiosis International University

Key Points

This research aims to develop an effective model for real-time fraud detection in payment systems.
Utilized Coupled Modular Simplicial Graph Neural Network (CMSGNN) architecture.
Implemented Snow Ablation Optimization (SAO) for improved prediction accuracy.
Applied Adaptive Morphological Wavelet Perona-Malik algorithm for noise removal.
Used Quokka Swarm Optimization (QukSO) for feature selection to enhance data quality.
Achieved precision of 99.1%, recall of 99.4%, and an F1-score of 99.2%.
Reported an accuracy of 99.5% and specificity of 99.3%.
Demonstrated effective real-time fraud detection capability.

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 Selection using Quokka Swarm Optimization (QukSO) is used to remove unnecessary features by keeping the most informative attributes and penalizing redundant or irrelevant ones. In the classification process, the Coupled Modular Simplicial Graph Neural Network (CMSGNN) is utilized, which inherits the advantages of Coupled Modular Neural Networks (CMNN) for modular learning and the Simplicial Graph Attention Network (SGAN) for efficient learning of higher-order topological relationships between transaction data. To enhance more accurately make predictions, the architecture adds Snow Ablation Optimization (SAO), which is the optimization of weights and reduction of misclassification error.The CMSGNN-SAO architecture yields enhanced flexibility, scalability, and reliability in detecting fraud versus non-fraud transactions. Experimental findings confirm its advantage in precision (99.1), recall (99.4), F1-score (99.2), accuracy (99.5), specificity (99.3), and ROC performance, making it a competent deep learning approach for real-time fraud detection (RTFD) in contemporary payment systems.

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