Credit Card Fraud Detection System Using Machine Learning

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Angela Makolo 1,* Tayo Adeboye 1

1. Department of Computer Science, University of Ibadan, Nigeria

* Corresponding author.


Received: 27 Feb. 2021 / Revised: 3 Apr. 2021 / Accepted: 19 Apr. 2021 / Published: 8 Aug. 2021

Index Terms

Credit card fraud, multivariate Gaussian distribution, genetic algorithm, artificial neural network, decision tree, support vector machine


The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

Cite This Paper

Angela Makolo, Tayo Adeboye, "Credit Card Fraud Detection System Using Machine Learning", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.4, pp.24-37, 2021. DOI:10.5815/ijitcs.2021.04.03


[1]Samaneh Sorournejad, Zahra Zojaji, Reza Ebrahimi Atani, and Amir Hassan Monadjemi (2016). A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective. arXiv:1611.6439
[2]Kehinde, James Sunday PhD. (2015). Banking Sector Technology Discrepancies: The Cost and Effect on Service Delivery. European Journal of Business and Management. ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) Vol.7, No.7
[3]V. Filippov, L. Mukhanov, and B. Shchukin (2008). Credit Card Fraud Detection System. 7th IEEE International Conference on Cybernetic Intelligent Systems, 1-6, 2008
[4]Amanze, B.C., and Onukwugha, C.G (2018). Credit Card Fraud Detection System in Nigeria Banks Using Adaptive Data Mining and Intelligent Agents: A Review. International journal of scientific & technology research, 7(7).
[5]Bart, B., Veronique V. & Wouter, V. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. John Wiley Sons Inc
[6]Reurink, A. (2016). Financial fraud: A literature review (No. 16/5). MPIfG Discussion Paper.
[7]Yifu, D. & Isabel, G. (2017). Using Uber Engineering to Combat Fraud in Real Time. Retrieved November 15, 2017, from
[8]Delamaire, L, Abdou, HAH and Pointon, J (2009). Credit card fraud and detection techniques: a review. Banks and systems, 4(2)
[9]Zareapoor, M., Seeja, K. R., & Alam, M. A. (2012). Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria. International Journal of Computer Applications, 52(3).
[10]Dal Pozzolo, A., & Bontempi, G. (2015). Adaptive machine learning for credit card fraud detection.
[11]Prakash, A., & Chandrasekar, C. (2013). A parameter optimized approach for improving Credit card fraud detection. International Journal of Computer Science, 10.
[12]Jha, S., Guillen, M., & Westland, J. C. (2012). Employing transaction aggregation strategy to detect credit card fraud. Expert systems with applications, 39(16), 12650-12657.
[13]MohdAvesh Z., Jabir D. & Ali Haider E. (2014). Credit Card Fraud Detection System Using Hidden Markov Model and K-Clustering. International Journal of Advanced Research in Computer and Communication Engineering. Vol. 3.
[14]Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88-105.
[15]O. M. Elzeki, M. F. Alrahmawy, Samir Elmougy, "A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.12, pp.20-33, 2019. DOI: 10.5815/ijisa.2019.12.03
[16]Hamdy M. Mousa, "Bat-Genetic Encryption Technique", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.11, pp.1-15, 2019. DOI: 10.5815/ijisa.2019.11.01
[17]Nasim Soltani Soulegan, Behrang Barekatain, Behzad Soleimani Neysiani, "MTC: Minimizing Time and Cost of Cloud Task Scheduling based on Customers and Providers Needs using Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.2, pp.38-51, 2021. DOI: 10.5815/ijisa.2021.02.03
[18]Behzad Soleimani Neysiani, Nasim Soltani, Reza Mofidi, Mohammad Hossein Nadimi-Shahraki, "Improving Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.2, pp.48-55, 2019. DOI: 10.5815/ijitcs.2019.02.06
[19]Jyoti S. Kulkarni, Rajankumar S. Bichkar, " Optimization in Image Fusion Using Genetic Algorithm", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.8, pp. 50-59, 2019.DOI: 10.5815/ijigsp.2019.08.05
[20]S. Gopa; Krishna Patro & Kishore Kumar Sahu (2015). Normalization: A Preprocessing Stage. arXiv:1503.06462v1[cs.OH]
[21]I. -C. Yeh & C. – H. Lien (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert systems with applications, 36 (2473-2480)
[22]Olufade F.W. Onifade, Joseph D. Akinyemi, Olashile S. Adebimpe,"A Recursive Binary Tree Method for Age Classification of Child Faces", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.10, pp.56-66, 2016.DOI: 10.5815/ijmecs.2016.10.08