An Intelligent Method for Improving Credit Card Fraud Detection Using a Hybrid LSTM and Deep Neural Network Framework

Ghulam Farooque; ANAM AHSAN; SANAA ASHIQ; MUHAMMAD  JUNAID; SAJID IQBAL; IFTIKHAR AHMED KHAN

Ghulam Farooque University of Lahore
ANAM AHSAN Department of Computer Science, The University of Lahore, Sargodha Campus, Sargodha 40162, Pakistan
SANAA ASHIQ Department of Software Engineering National University of Modern Languages, Rawalpindi, Pakistan
MUHAMMAD JUNAID Department of Computer Science, The University of Lahore, Sargodha Campus, Sargodha 40162, Pakistan
SAJID IQBAL Department of Computer Science and IT, The University of Lahore, Lahore 54000, Pakistan
IFTIKHAR AHMED KHAN Department of Computer Science and IT, The University of Lahore, Lahore 54000, Pakistan

Keywords: Cybersecurity, Imbalanced Data Handling, Real-Time Fraud Prevention, Credit Card Fraud Detection, Machine Learning

Abstract

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E-commerce has caused a great transformation in the chain of operations through which companies all over the world transact their businesses. However, with the rapid increase in online shopping, the prevalence of online fraud, particularly credit card fraud has emerged as one of the major security threats connected with e-commerce. The classical models of fraud detection easily address the problems of imbalanced data, pattern of the poorly-sequentially recorded data, and the need to detect the fraud instantly. To address the challenges mentioned in this study, a hybrid architecture which is a fusion of Long Short-Term Memory (LSTM) unit and Deep Neural Network (DNN) modules is propsoed. The DNN component is meant to discover complex interrelatedness of diverse features. Whereas, the LSTM layer establishes a temporal connection which exists in a series of dealings. The preprocessing stage applies the method of the Synthetic Minority Over-Sampling Technique (SMOTE) to solve the issue of unrepresentative classes. The model is tested on the publicly available credit card frauds dataset. It is observed that the proposed model shows a better performance with 99.6% accuracy, 94.5% precision, recall of 91.2% and ROC-AUC of 97.3%, respectively. The comparative study reveals that the hybrid model is superior to the traditional algorithms, including logistic regression, decision trees, LightGBM, and single-created LSTM models, with regard to prediction performance. The presentation of the confusion matrices, the precision-recall curves, and the learning curves is also used to justify the measures of the soundness of the model and its generalizability, without showing the training and validation loss. To conclude, all of these visual tests confirm the reliability of the system under various conditions of the working environment. On the whole, the study adds significantly to the development of a more efficient and scalable fraud detection system, the overall purpose of which is to enhance the level of safety of virtual transaction setups and employ it to other industrial domains, such as energy.

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