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Baku Agyo Raphael, Bisen Gambo Adashu, Andrew Ishaku Wreford
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- Lecturer, Lecturer, Lecturer, Department of Computer Science, Federal University, Department of Computer Science, Kwararafa University, Department of Computer Science, Federal University, Wukari, Wukari, Wukari, Nigeria, Nigeria, Nigeria
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Abstract
nFraud has posed a significant challenge for merchants, especially in the online business sector, over the course of many years. This is primarily due to the advancements in technology that have made credit card transactions a common method of payment. Credit card fraud refers to the unauthorized use of a credit card by an individual for personal purposes, without the owner’s consent and with no intention of paying for the incurred expenses or engaging in deceptive activities to gain financial advantage. Given the efforts made by fraudsters to disguise their transactions as legitimate, this study introduced an Artificial Neural Network model powered by a machine learning algorithm to identify and detect fraudulent activities in credit card transactions. The researcher effectively filtered and cleanses the dataset sourced from Kaggle machine learning repository selection techniques. The experiment was setup on a 64-bit Windows OS on an Intel (R) Core (TM) i5-3530 QM CPU @ 2.40 GHZ. Python 3.10 via Anaconda environment using Jupiter notebook were used as the integrated development environment. Dataset exploration, reading, scaling and performance evaluation were done successfully. The study result found prediction accuracy of 0.9184 value which is equivalent to 92% at step 716 with 4.6 ms conducted per step and also loss metric based on binary entropy of 2.0%. The study recommended future research and advancement in Artificial Neural Network by hybridising Deep Neutral Network DNN and Relu Neural Network for multi perception optimised performance.
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Keywords: Entropy, classifier, Credit card-fraud, ANN, Artificial Neural Network,
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References
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1. Bhatla, T. P., Prabhu, V., and Dua, A. (2003). Understanding Credit Card Frauds, Cards business review 1 (6) (2003).
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7. Kuldeep, R., Chu Kiong, L., Manjeevan, S., Chee, P.L., and Asoke, K.N. (2018). Credit card Fraud detection using AdaBoost. DOI 10.1109/ACCESS.2018.2806420. http://creativecommons.org/licenses/by/3.0/.
8. John, O.A., Adebayo, O.A., and Samuel, A.O. (2017). Credit Card Fraud Detection using machine learning techniques: A comparative analysis. International Journal of Soft Computing and Engineering (IJSCE). 978-1-5090-4642-3,1, 32-38.
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10. Faraji, Z. (2022). A Review of Machine Learning Applications for Credit Card Fraud Detection with A Case study. SEISENSE Journal of Management, 5(1), 49–59. https://doi.org/10.33215/sjom.v5i1.770
11. Bommala H., Basha R.M., Rajarao B., Sangeetha K. (2022). An Innovative Model-Based Approach for Credit Card Fraud Detection Using K-Nearest. International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_19
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International Journal of Algorithms Design and Analysis Review
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| Volume | 01 |
| Issue | 01 |
| Received | June 21, 2023 |
| Accepted | July 3, 2023 |
| Published | July 25, 2023 |
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