Citation :

Moosa Swarnalatha, Neelakantappa M, "Intelligent Credit Card Fraud Detection based on Sea Horse Optimization and Deep Sequential Learning," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 14-25, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P102

Abstract

Recently, credit card fraud has increased a significant threat to financial institutions and consumers, thus a robust and intelligent detection system is essential to prevent fraudulent activities efficiently. In this research, an optimized deep learning model, namely Sea Horse Optimization (SHO), based on feature selection and Long Short-Term Memory (LSTM) with an attention mechanism, based detection model to efficiently capture the temporal sequence in credit card transactions. The proposed SHO-LSTM framework is trained and evaluated on the Kaggle Credit Card Fraud Detection (CCFD) dataset, comprising 284,807 transactions. Additionally, a sampling technique, namely Synthetic Minority Oversampling Technique, is used in this research for solving the class imbalance issue effectively and enhance detection performance. Experimental results of the proposed model demonstrate that the SHO-LSTM model achieves superior performance with an accuracy of 99.98%, which is higher than the existing fraud detection methods such as CNN-SVM, CSO-DNN, and CSO-GRU. These findings underscore the effectiveness of combining metaheuristic optimization with deep learning for fraud detection in highly imbalanced datasets.

Keywords

Credit card transaction, Fraud detection, Long Short-Term Memory, Sea-Horse Optimization, Synthetic Minority Oversampling Technique.

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