A Comparative Study on Machine Learning and Fuzzy Logic-Based Approach for Enhancing Credit Card Fraud Detection

Authors

  • Jayanthi. G Associate Professor, Department of Computer Science and Engineering (IoT), Saveetha Engineering College, Chennai
  • Deepthi. P 2Assistant Professor, Department of CSE, Madanapalle Institute of Technology & Science, Kadiri Road Angallu Madanapalle, Andhrapradesh, 517325
  • B. Nageswara Rao Faculty of Mathematics, School of Technology, Apollo University Murukambathu, Chittooru (Dist), A.p, India, Pin: 517127
  • M. Bharathiraja Professor, Automobile Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode
  • Logapriya. A Assistant professor, Artificial Intelligence and Data Science Panimalar Engineering College, Chennai

Keywords:

Credit card fraud detection, Machine learning models, Artificial neural network, Support vector machine, Random Forest

Abstract

The present research evaluates the efficacy of several machine learning models in credit card fraud detection, employing data sets of 284,407 transactions produced from online platforms. Careful data processing includes cleansing, scaling, mechanical properties, data imbalance management, and transient characteristics After preprocessing, five models— Artificial Neural Network (ANN), Support Vector Machine (SVM), Random were trained Forest (RF), Decision Tree (DT), and Naive Bayes (NB)—were assessed. Notably, ANN demonstrated an amazing performance of 97.6% accuracy, followed closely by SVM 95.5%, RF 94.5%, DT 92.3%, and NB 88.9% with the confusion matrices indicating high accuracy, true negatives, false positives, and false positives of each sample. It also gave little insight into the capacity to effectively identify false negatives. While ANN exhibited a very accurate, balanced detection of fraudulent and valid transactions, DT-NB showed a number of misclassifications rising disclosure. These arise from careful selection of machine learning models for credit card fraud detection, micro -And underline the significance of integration, with factors such as accuracy, translation, computational economy, and the etc. included. The study offers the standards and principles required to construct powerful and comprehensive credit card fraud detection systems, leading to gains in financial security and continually improving fraud prevention tactics.

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References

K. Kandasamy, S. Srinivas, K. Achuthan, and V. P. Rangan, “IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process,” Eurasip Journal on Information Security, vol. 2020, no. 1, 2020, doi: 10.1186/s13635-020-00111-0.

M. Preetha et al., “Efficient Re-clustering with Novel Fuzzy Based Grey Wolf Optimization for Hotspot Issue Mitigation and Network Lifetime Enhancement,” Journal of Ad Hoc & Sensor Wireless Networks, Vol. 56, Issue 4, page No-273-297, Sep 2023

P. Vanini, S. Rossi, E. Zvizdic, and T. Domenig, “Online payment fraud: from anomaly detection to risk management,” Financial Innovation, vol. 9, no. 1, 2023, doi: 10.1186/s40854-023-00470-w.

T. Zhu et al., “RiskCog: Unobtrusive real-time user authentication on mobile devices in the wild,” IEEE Transactions on Mobile Computing, vol. 19, no. 2, pp. 466–483, 2020, doi: 10.1109/TMC.2019.2892440.

H. Alam et al., “IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning,” Wireless Communications and Mobile Computing, vol. 2023, 2023, doi: 10.1155/2023/1175450.

S. Kyeong and J. Shin, “Two-stage credit scoring using Bayesian approach,” Journal of Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00665-5.

I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” Journal of Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00318-5.

J. Petch et al., “Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study,” Contemporary Clinical Trials, vol. 122, no. September, p. 106963, 2022, doi: 10.1016/j.cct.2022.106963.

Q. Zhao, K. Chen, T. Li, Y. Yang, and X. F. Wang, “Detecting telecommunication fraud by understanding the contents of a call,” Cybersecurity, vol. 1, no. 1, pp. 1–12, 2018, doi: 10.1186/s42400-018-0008-5.

M.Preetha et al., “A Survey on Microcontroller based Machine to Machine Interaction with Temperature Control System”, International Journal of Advance Engineering and Research Development (IJAERD), Vol.5, No 2, pg.159-162, February 2018. Print ISSN 2348 - 6406, Online ISSN 2348 – 4470

M. E. Edge and P. R. Falcone Sampaio, “The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams,” Expert Systems with Applications, vol. 39, no. 11, pp. 9966–9985, 2012, doi: 10.1016/j.eswa.2012.01.143.

I. Priyadarshini et al., “A new enhanced cyber security framework for medical cyber physical systems,” Software-Intensive Cyber-Physical Systems, vol. 35, no. 3–4, pp. 159–183, 2021, doi: 10.1007/s00450-021-00427-3.

K. A. Alaghbari, M. H. M. Saad, A. Hussain, and M. R. Alam, “Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations,” Journal of Cloud Computing, vol. 11, no. 1, 2022, doi: 10.1186/s13677-022-00338-x.

Preetha M et al., ”A Survey on Entry Restriction System for the fake server Scheme”, International journal of Research and Engineering (IJRE), Vol.4, No 2, pg.41-44, February 2017. ISSN:2348-7860(0).

W. Hilal, S. A. Gadsden, and J. Yawney, “Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances,” Expert Systems with Applications, vol. 193, p. 116429, 2022, doi: 10.1016/j.eswa.2021.116429.

E. Ileberi, Y. Sun, and Z. Wang, “A machine learning based credit card fraud detection using the GA algorithm for feature selection,” Journal of Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00573-8.

M. Ala’raj, M. F. Abbod, and M. Majdalawieh, “Modelling customers credit card behaviour using bidirectional LSTM neural networks,” Journal of Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00461-7.

M. Preetha et al., “A Preliminary Analysis by using FCGA for Developing Low Power Neural Network Controller Autonomous Mobile Robot Navigation”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN:2147-6799

Z. Salekshahrezaee, J. L. Leevy, and T. M. Khoshgoftaar, “The effect of feature extraction and data sampling on credit card fraud detection,” Journal of Big Data, vol. 10, no. 1, 2023, doi: 10.1186/s40537-023-00684-w.

R. M. Dantas, R. Firdaus, F. Jaleel, P. Neves Mata, M. N. Mata, and G. Li, “Systemic Acquired Critique of Credit Card Deception Exposure through Machine Learning,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 4, 2022, doi: 10.3390/joitmc8040192.

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,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 1, pp. 145–174, 2023, doi: 10.1016/j.jksuci.2022.11.008.

P. Gupta, A. Varshney, M. R. Khan, R. Ahmed, M. Shuaib, and S. Alam, “Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques,” Procedia Computer Science, vol. 218, pp. 2575–2584, 2023, doi: 10.1016/j.procs.2023.01.231.

M. Preetha et al., “Deep Learning-Driven Real-Time Multimodal Healthcare Data Synthesis”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN:2147-6799, Vol.12, Issue 5, page No-360-369, 2024

T. A. Olowookere and O. S. Adewale, “A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach,” Scientific African, vol. 8, p. e00464, 2020, doi: 10.1016/j.sciaf.2020.e00464.

M. Habibpour, H. Gharoun, M. Mehdipour, and A. Tajally, “Engineering Applications of Artificial Intelligence Uncertainty-aware credit card fraud detection using deep learning,” Engineering Applications of Artificial Intelligence, vol. 123, no. January, p. 106248, 2023, doi: 10.1016/j.engappai.2023.106248.

J. K. Afriyie et al., “A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions,” Decision Analytics Journal, vol. 6, no. December 2022, p. 100163, 2023, doi: 10.1016/j.dajour.2023.100163.

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Published

12.01.2024

How to Cite

G, J., P, D., Rao, B. N. ., Bharathiraja, M. ., & A, L. (2024). A Comparative Study on Machine Learning and Fuzzy Logic-Based Approach for Enhancing Credit Card Fraud Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 192–199. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4504

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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