Research Article

, 28 Aug 2025 | 10.6234610.62346/ijcn_q3_v13_no3_25_02

Year : 2025 | Volume: 13 | Issue: 3 | Pages : 1-5

Hybrid LSTM- SVM Model for Improved Credit Card Fraud Detection: A Comparative Study with KNN, Naïve Bayes, SVM and Logistic Regression

Antro Monica Sanjas¹ *, A.Merry Ida, S.G.Santhiya, P.Anitha, S.Angel Nithya

1Anna University Chennai, Assistant Professor, Dept. of CSE(AIML), Loyola Institute of Technology and Science, Thovalai, Nagarcoil, India, IN

The growing number and complexity of fraudulent transactions make detecting credit card theft a crucial task in the banking industry. The identification of fraud has made extensive use of traditional machine learning techniques including K-Nearest Neighbor (KNN), Naïve Bayes, SVM, and Logistic Regression. However, these models frequently encounter difficulties when dealing with the temporal correlations and sequential patterns present in transaction data. Long Short-Term Memory (LSTM) networks and Support Vector Machine (SVM) classifiers are combined in this study's hybrid technique to enhance fraud detection capabilities. The LSTM network generates high-level feature representations by efficiently capturing temporal correlations and sequential patterns in transaction sequences. An SVM, which offers strong decision boundaries and improved generalization on unbalanced datasets, is then used to classify these features. The suggested LSTM ? SVM hybrid model performs better than KNN, Naïve Bayes, regular SVM, and Logistic Regression in terms of accuracy, precision, recall, and F1-score, according to experiments done on a benchmark credit card fraud dataset. The findings show that using temporal sequence modeling in conjunction with SVM classification greatly improves the identification of fraudulent activity, which makes it a viable strategy for practical financial security applications.

Conclusion

In conclusion, the main objective of this project was to find the most suited model in credit card fraud detection in terms of the machine learning techniques chosen for the project, and it was met by building the four models and finding the accuracies of them all, the best model in terms of accuracies is LSTM-SVM which scored 99.95% with only 51 misclassified instances. I believe that using the model will help in decreasing the amount of credit card fraud and increase the customers satisfaction as it will provide them with better experience in addition to feeling secure.

The outcomes unequivocally show that the LSTM-SVM hybrid model outperforms conventional classifiers in credit card fraud detection. Superior fraud detection accuracy and robustness in unbalanced datasets are achieved by its capacity to capture sequential dependencies and provide discriminative features for SVM classification.

References

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Keywords: LSTM, SVM, Hybrid Model, Credit Card Fraud Detection, Temporal Feature Extraction, Machine Learning

Citation: Antro Monica Sanjas*, Antro Monica Sanjas ( 2025), Hybrid LSTM- SVM Model for Improved Credit Card Fraud Detection: A Comparative Study with KNN, Naïve Bayes, SVM and Logistic Regression . , 13(3): 1-5

Received: 20/08/2025; Accepted: 25/08/2025;
Published: 28/08/2025

Edited by:

Mr.ERES JOURNALS

Reviewed by:

*Correspondence: Antro Monica Sanjas, monica.cse@lites.edu.in

Excellant Research in Engineeringand Science Publications

Research Article

Hybrid LSTM- SVM Model for Improved Credit Card Fraud Detection: A Comparative Study with KNN, Naïve Bayes, SVM and Logistic Regression

Conclusion

References

Excellant Research in Engineering
and Science Publications