TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Advancing financial security : A hybrid fusion of neural network and rule-based system for credit card fraud detection

* , , , , , ,

* Corresponding author · click or hover a name for details

pp. 521–530Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1932XML
Received:
13 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1932
Pages:
521–530

Abstract

Credit card fraud presents a serious threat to financial institutions, causing substantial financial and reputational damage. To address this, the proposed method combines a neural network with a rule-based system to enhance fraud detection. This hybrid model effectively examines credit card transactions by capturing complex patterns and integrating expert insights, improving accuracy and minimizing false positives. By leveraging human-understandable rules and domain knowledge, the approach enhances transparency in decision-making processes. This innovative solution represents a significant advancement in fraud detection, helping financial institutions mitigate losses while ensuring greater trust and security in financial transactions.

Keywords

Subject Classifications

68T0568T10

References

[1] A. Fernandez, S. Garcia, F. Herrera, and N. V. Chawla, “SMOTE for Learning From Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary,” Journal of Artificial Intelligence Research, vol. 61, pp. 863–905, Apr. (2018), doi: 10.1613/jair.1.11192.
[2] T. J. Jebaseeli, R. Venkatesan, and K. Ramalakshmi, “Fraud Detection for Credit Card Transactions Using Random Forest Algorithm,” in Advances in intelligent systems and computing, pp. 189–197 (2020). doi: 10.1007/978-981-15-5285-4_18.
[3] Z. Kazemi and H. Zarrabi, “Using Deep Networks for Fraud Detection in the Credit Card Transactions,” Proceedings of IEEE 4th International Conference on Knowledge-Based Engineering and Innovation, pp. 0630–0633, Dec. (2017), doi: 10.1109/kbei.2017.8324876.
[4] J. Kumar and V. Saxena, “Rule-Based Credit Card Fraud Detection Using User’s Keystroke Behavior,” in Lecture notes in networks and systems, pp. 469–480 (2022). doi: 10.1007/978-981-19-0707-4_43.
[5] S. KSRK, “Local Gradient Dual Coding Book (LGDCB) Framework for Effective Texture Classification,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 6, no. 4, pp. 1680–1687, Aug. (2019), doi: 10.30534/ijatcse/2019/95842019.
[6] N. Perveen, D. Roy, and K. M. Chalavadi, “Facial expression recognition in videos using dynamic kernels,” IEEE Transactions on Image Processing, vol. 29, pp. 8316–8325, Jan. (2020), doi: 10.1109/tip.2020.3011846.
[7] K. R. Prasad, “Big Data sentiment Analysis using Distributed Computing approach,” in Advances in intelligent systems and computing, pp. 689–699 (2018). doi: 10.1007/978-981-13-1580-0_66.
[8] A. Pumsirirat and L. Yan, “Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder and Restricted Boltzmann Machine,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, Jan. (2018), doi: 10.14569/ijacsa.2018.090103.
[9] T. R. Pillai, I. A. T. Hashem, S. N. Brohi, S. Kaur, and M. Marjani, “Credit Card Fraud Detection Using Deep Learning Technique,” Proceedings of 4th International Conference on Advances in Computing, Communication and Automation, pp. 1–6, Oct. (2018), doi: 10.1109/icaccaf.2018.8776797.
[10] D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic, and A. Anderla, “Credit Card Fraud Detection - Machine Learning methods,” 18th International Symposium INFOTEH-JAHORINA (INFOTEH), Mar. (2019), doi: 10.1109/infoteh.2019.8717766.
[11] X. Yu, X. Li, Y. Dong, and R. Zheng, “A Deep Neural Network Algorithm for Detecting Credit Card Fraud,” International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Jun. (2020), doi: 10.1109/icbaie49996.2020.00045.

Views: 247Downloads: 84Citations: 0