Authors : Abubakar Umar; Salisu Mamman Abdulrahman; Sani Danjuma; Mohammed Kabir Daud; Musa Adamu Wakili; Abdulmajid Babangida Umar; Haris Abdullahi Shehu

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/ydd4ayyv

Scribd : https://tinyurl.com/4hy8zpdh

DOI : https://doi.org/10.38124/ijisrt/25dec932

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Abstract : The advancement of electronic banking has increased the acceptance and use of credit card rendering it as one of the most universally accepted method of payment globally. The incidence of transaction fraud required an effective detection technique to protect customers and financial companies from being trapped by fraudsters. The process of fraud detection, which pertains to the recognition of illicit activities within banking systems, is critical for ensuring financial stability, protecting customer interests, managing institutional reputation, and complying with regulatory requirements. The methodologies encompassing machine learning and deep learning have seen extensive application in addressing issues related to credit card fraud; however, a significant proportion of these methodologies encounter challenges, including erroneous classification and false positives, among other complications. Recent research shows that fraudsters persist in employing novel methodologies in their illicit activities by altering the characteristics or trends in their deceptive practices, thereby rendering fraudulent transactions indistinguishable from genuine ones in an effort to evade detection by current detection mechanisms. To optimize model precision and enhance fraud detection using deep learning feature selection (FS) is of paramount importance. This will alleviate the adverse impacts of noisy, irrelevant and redundant attributes present within the dataset. This research work proposed a new approach that uses Flower Pollination Algorithm (FPA) with Spike Neural Network (SNN) a deep learning technique called FPA-SNN for credit card fraud detection. Four datasets were used to implement the proposed approach, two of the datasets are highly unbalanced with a <1% positive class. To improve classification accuracy and precision we used Synthetic Minority Oversampling Technique (SMOTE) to solve the imbalance problem in the datasets. Realizing that the vast majority of studies in credit card fraud detection uses very few performance metrics for evaluating various machine learning and deep learning algorithms, we utilized multiple evaluation metrics; Accuracy, Precision, Recall, F1-score, the area under curve and the receiver operating characteristic curve (AUC_ROC), and Matthew’s Correlation Coefficient (MCC) to test and evaluate the performance of our proposed model. Our Proposed model performed significantly well with highest MCC greater than 97 percent, as well as AUC-ROC greater than 99.9 percent which shows how robust the model is in feature selection and classification.

Keywords : Credit Card Fraud Detection, Nature Inspired Algorithms, Deep Learning, Machine Learning, Flower Pollination Algorithm (PFA), Spike Neural Network (SNN), Feature Selection and Classification.

References :

1. Y. Song, O. Escobar, U. Arzubiaga, and A. De Massis, “The digital transformation of a traditional market into an entrepreneurial ecosystem,” Review of Managerial Science, vol. 16, no. 1, pp. 65–88, 2022, doi: 10.1007/s11846-020-00438-5.
2. V. F. Rodrigues et al., “Fraud detection and prevention in e-commerce: A systematic literature review,” Electron Commer Res Appl, vol. 56, Nov. 2022, doi: 10.1016/j.elerap.2022.101207.
3. P. Sharma and S. Pote, “Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto encoder,” Int J Eng Adv Technol, vol. 9, no. 5, pp. 1140–1143, 2020, doi: 10.35940/ijeat.e9934.069520.
4. W. Lovo, “JMU Scholarly Commons Detecting credit card fraud : An analysis of fraud detection techniques,” 2020.
5. T. Pourhabibi, K. L. Ong, B. H. Kam, and Y. L. Boo, “Fraud detection: A systematic literature review of graph-based anomaly detection approaches,” Decis Support Syst, vol. 133, no. April, p. 113303, 2020, doi: 10.1016/j.dss.2020.113303.
6. R. Juniper, “COMBATTING ONLINE PAYMENT FRAUD Whitepaper,” 2024.
7. Y. T. Lei, C. Q. Ma, Y. S. Ren, X. Q. Chen, S. Narayan, and A. N. Q. Huynh, “A distributed deep neural network model for credit card fraud detection,” Financ Res Lett, vol. 58, no. PC, p. 104547, 2023, doi: 10.1016/j.frl.2023.104547.
8. D. Sachar, “Optimizing Transaction Fraud Detection: A Comparative Study of Nature-Inspired Algorithms for Feature Selection,” in 2025 IEEE Systems and Information Engineering Design Symposium, SIEDS 2025, Institute of Electrical and Electronics Engineers Inc., 2025, pp. 392–397. doi: 10.1109/SIEDS65500.2025.11021207.
9. Z. Li, M. Huang, G. Liu, and C. Jiang, “A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection,” Expert Syst Appl, vol. 175, no. February, p. 114750, 2021, doi: 10.1016/j.eswa.2021.114750.
10. F. Carcillo, Y. A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, and G. Bontempi, “Combining unsupervised and supervised learning in credit card fraud detection,” Inf Sci (N Y), vol. 557, pp. 317–331, May 2021, doi: 10.1016/j.ins.2019.05.042.
11. H. Tabrizchi and J. Razmara, “Credit card fraud detection using hybridization of isolation forest with grey wolf optimizer algorithm,” Soft comput, vol. 2, no. Ml, 2024, doi: 10.1007/s00500-024-09772-2.
12. F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms,” IEEE Access, vol. 10, pp. 39700–39715, 2022, doi: 10.1109/ACCESS.2022.3166891.
13. S. Bakhtiari, Z. Nasiri, and J. Vahidi, “Credit card fraud detection using ensemble data mining methods,” Multimed Tools Appl, vol. 82, no. 19, pp. 29057–29075, 2023, doi: 10.1007/s11042-023-14698-2.
14. A. Cherif, A. Badhib, H. Ammar, S. Alshehri, M. Kalkatawi, and A. Imine, “Credit card fraud detection in the era of disruptive technologies: A systematic review,” Jan. 01, 2023, King Saud bin Abdulaziz University. doi: 10.1016/j.jksuci.2022.11.008.
15. E. A. L. M. Btoush, X. Zhou, R. Gururajan, K. C. Chan, R. Genrich, and P. Sankaran, “A systematic review of literature on credit card cyber fraud detection using machine and deep learning,” PeerJ Comput Sci, vol. 9, pp. 1–66, 2023, doi: 10.7717/PEERJ-CS.1278.
16. J. Huang, “Credit Card Transaction Fraud Using Machine Learning Algorithms,” vol. 116, no. Icesed 2019, pp. 82–91, 2020, doi: 10.2991/icesed-19.2020.14.
17. N. S. Alfaiz and S. M. Fati, “Enhanced Credit Card Fraud Detection Model Using Machine Learning,” Electronics (Switzerland), vol. 11, no. 4, Feb. 2022, doi: 10.3390/electronics11040662.
18. S. Singh and A. Maheshwari, “Credit Card Fraud Detection,” Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, no. October, pp. 209–213, 2022, doi: 10.1109/ICAC3N56670.2022.10074052.
19. S. Negi, S. K. Das, and R. Bodh, “Credit Card Fraud Detection using Deep and Machine Learning,” Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, no. Icaaic, pp. 455–461, 2022, doi: 10.1109/ICAAIC53929.2022.9792941.
20. R. Chhabra, S. Goswami, and R. Kumar, “A voting ensemble machine learning based credit card fraud detection using highly imbalance data,” Multimed Tools Appl, vol. 83, no. 18, pp. 54729–54753, 2024, doi: 10.1007/s11042-023-17766-9.
21. K. I. Alkhatib, A. I. Al-aiad, M. H. Almahmoud, and O. N. Elayan, “Credit Card Fraud Detection Based on Deep Neural Network Approach,” no. May, 2021, doi: 10.1109/ICICS52457.2021.9464555.
22. F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms,” IEEE Access, vol. 10, pp. 39700–39715, 2022, doi: 10.1109/ACCESS.2022.3166891.
23. G. Zioviris, K. Kolomvatsos, and G. Stamoulis, “Credit card fraud detection using a deep learning multistage model,” Journal of Supercomputing, vol. 78, no. 12, pp. 14571–14596, 2022, doi: 10.1007/s11227-022-04465-9.
24. R. Bin Sulaiman, V. Schetinin, and P. Sant, “Review of Machine Learning Approach on Credit Card Fraud Detection,” Human-Centric Intelligent Systems, vol. 2, no. 1–2, pp. 55–68, 2022, doi: 10.1007/s44230-022-00004-0.
25. T. K. Dang and T. Ha, “A Comprehensive Fraud Detection for Credit Card Transactions in Federated Averaging,” SN Comput Sci, vol. 5, no. 5, 2024, doi: 10.1007/s42979-024-02898-y.
26. M. Abdul Salam, K. M. Fouad, D. L. Elbably, and S. M. Elsayed, “Federated learning model for credit card fraud detection with data balancing techniques,” Neural Comput Appl, vol. 36, no. 11, pp. 6231–6256, 2024, doi: 10.1007/s00521-023-09410-2.
27. Y. Alghofaili, A. Albattah, and M. A. Rassam, “A Financial Fraud Detection Model Based on LSTM Deep Learning Technique,” Journal of Applied Security Research, vol. 15, no. 4, pp. 498–516, 2020, doi: 10.1080/19361610.2020.1815491.
28. T. T. Nguyen, H. Tahir, M. Abdelrazek, and A. Babar, “Deep Learning Methods for Credit Card Fraud Detection,” p. 8, 2020.
29. D. Sehrawat and Y. Singh, “Auto-Encoder and LSTM-Based Credit Card Fraud Detection,” SN Comput Sci, vol. 4, no. 5, Jul. 2023, doi: 10.1007/s42979-023-01977-w.
30. I. Benchaji, S. Douzi, B. El Ouahidi, and J. Jaafari, “Enhanced credit card fraud detection based on attention mechanism and LSTM deep model,” J Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-021-00541-8.
31. M. Ayoub, T. Abdelhamid, and J. Khalid, “Granular computing framework for credit card fraud detection,” May 01, 2025, Elsevier B.V. doi: 10.1016/j.aej.2025.02.019.
32. S. E. Sorour, K. M. AlBarrak, A. A. Abohany, and A. A. A. El-Mageed, “Credit card fraud detection using the brown bear optimization algorithm,” Alexandria Engineering Journal, vol. 104, no. May, pp. 171–192, 2024, doi: 10.1016/j.aej.2024.06.040.
33. M. Abdel-Basset and L. A. Shawky, “Flower pollination algorithm: a comprehensive review,” Dec. 01, 2019, Springer Netherlands. doi: 10.1007/s10462-018-9624-4.
34. W. Xu, Y. Wang, D. Yan, and Z. Ji, “Flower Pollination Algorithm for Multi-Objective Fuzzy Flexible Job Shop Scheduling,” Xitong Fangzhen Xuebao / Journal of System Simulation, vol. 30, no. 11, pp. 4403–4412, Nov. 2018, doi: 10.16182/j.issn1004731x.joss.201811042.
35. Z. A. A. Alyasseri, A. T. Khader, M. A. Al-Betar, M. A. Awadallah, and X. S. Yang, “Variants of the flower pollination algorithm: A review,” in Studies in Computational Intelligence, vol. 744, Springer Verlag, 2018, pp. 91–118. doi: 10.1007/978-3-319-67669-2_5.
36. X.-S. Yang, “Flower Pollination Algorithm for Global Optimization,” Dec. 2013, doi: 10.1007/978-3-642-32894-7_27.
37. K. Yamazaki, V. K. Vo-Ho, D. Bulsara, and N. Le, “Spiking Neural Networks and Their Applications: A Review,” Brain Sci, vol. 12, no. 7, pp. 1–30, 2022, doi: 10.3390/brainsci12070863.
38. J. D. Nunes, M. Carvalho, D. Carneiro, and J. S. Cardoso, “Spiking Neural Networks: A Survey,” IEEE Access, vol. 10, pp. 60738–60764, 2022, doi: 10.1109/ACCESS.2022.3179968.
39. A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. Maida, “Deep learning in spiking neural networks,” Neural Networks, vol. 111, pp. 47–63, 2019, doi: 10.1016/j.neunet.2018.12.002.
40. M. Abdul Salam, K. M. Fouad, D. L. Elbably, and S. M. Elsayed, “Federated learning model for credit card fraud detection with data balancing techniques,” Neural Comput Appl, vol. 36, no. 11, pp. 6231–6256, 2024, doi: 10.1007/s00521-023-09410-2.
41. T. K. Dang and T. Ha, “A Comprehensive Fraud Detection for Credit Card Transactions in Federated Averaging,” SN Comput Sci, vol. 5, no. 5, 2024, doi: 10.1007/s42979-024-02898-y.
42. B. Alshawi, “Utilizing GANs for Credit Card Fraud Detection: A Comparison of Supervised Learning Algorithms,” Engineering, Technology and Applied Science Research, vol. 13, no. 6, pp. 12264–12270, 2023, doi: 10.48084/etasr.6434.
43. H. C. Du, L. Lv, H. Wang, and A. Guo, “A novel method for detecting credit card fraud problems,” PLoS One, vol. 19, no. 3 March, pp. 1–26, 2024, doi: 10.1371/journal.pone.0294537.
44. W. Liu, X. Wang, and W. Peng, “State of the Art: Secure Mobile Payment,” IEEE Access, vol. 8, pp. 13898–13914, 2020, doi: 10.1109/ACCESS.2019.2963480.
45. A. Cherif, A. Badhib, H. Ammar, S. Alshehri, M. Kalkatawi, and A. Imine, “Credit card fraud detection in the era of disruptive technologies: A systematic review,” Jan. 01, 2023, King Saud bin Abdulaziz University. doi: 10.1016/j.jksuci.2022.11.008.
46. R. Bin Sulaiman, V. Schetinin, and P. Sant, “Review of Machine Learning Approach on Credit Card Fraud Detection,” Human-Centric Intelligent Systems, vol. 2, no. 1–2, pp. 55–68, 2022, doi: 10.1007/s44230-022-00004-0.
47. G. Zioviris, K. Kolomvatsos, and G. Stamoulis, “Credit card fraud detection using a deep learning multistage model,” Journal of Supercomputing, vol. 78, no. 12, pp. 14571–14596, 2022, doi: 10.1007/s11227-022-04465-9.
48. Q. Zhu, “On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset,” Pattern Recognit Lett, vol. 136, pp. 71–80, Aug. 2020, doi: 10.1016/j.patrec.2020.03.030.