Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning

Mashael Maashi (), Bayan Alabduallah and Fadoua Kouki
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Mashael Maashi: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia
Bayan Alabduallah: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Fadoua Kouki: Department of Financial and Banking Sciences, Applied College at Muhail Aseer, King Khalid University, Abha 62529, Saudi Arabia

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches.

Keywords: sustainability; credit card fraud detection; ensemble learning; feature selection; cybersecurity; Garra Rufa Fish optimizer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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