Comparative Assessment of Fraudulent Financial Transactions using the Machine Learning Algorithms Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest
Received: 8 May 2024 | Revised: 28 May 2024 | Accepted: 12 June 2024 | Online: 2 August 2024
Corresponding author: Wirapong Chansanam
Abstract
Today, fast-paced technology plays an important role in financial transactions, especially in payment-related digital habits. As fraud is a major concern in online payments, many machine-learning approaches have been proposed to detect and prevent fraudulent payment transactions. This study aimed to evaluate Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest in detecting fraudulent payment transactions. The results show that Random Forest, K-Nearest Neighbor, Decision Tree, and Logistic regression achieved total accuracy rates exceeding 99%. However, such impressive results do not necessarily indicate satisfactory performance. The results highlight the need to detect fraudulent transactions and investigate specific improvements to effectively manage and minimize unexpected financial transaction fraud.
Keywords:
machine learning algorithms, data mining, data analytics, customer payment transactionDownloads
References
N. I. Mustika, B. Nenda, and D. Ramadhan, "Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company," Asia Pacific Fraud Journal, vol. 6, no. 2, pp. 213–221, Dec. 2021.
K. Khando, M. S. Islam, and S. Gao, "The Emerging Technologies of Digital Payments and Associated Challenges: A Systematic Literature Review," Future Internet, vol. 15, no. 1, Jan. 2023, Art. no. 21.
J. K. Afriyie et al., "A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions," Decision Analytics Journal, vol. 6, Mar. 2023, Art. no. 100163.
S. Gold, "The evolution of payment card fraud," Computer Fraud & Security, vol. 2014, no. 3, pp. 12–17, Mar. 2014.
H. Alizadeh and B. M. Bidgoli, “Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty,” Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1235–1240, Dec. 2016.
R. Y. R. Abdaljawad, T. Obaid, and S. S. Abu-Naser, "Fraudulent Financial Transactions Detection Using Machine Learning," in 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA), Taiz, Yemen, Oct. 2023.
T. C. Tran and T. K. Dang, "Machine Learning for Prediction of Imbalanced Data: Credit Fraud Detection," in 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), Seoul, Korea (South), Jan. 2021, pp. 1–7.
K. S. Lim, L. H. Lee, and Y. W. Sim, "A Review of Machine Learning Algorithms for Fraud Detection in Credit Card Transaction," International Journal of Computer Science & Network Security, vol. 21, no. 9, pp. 31–40, 2021.
O. Kolodiziev, A. Mints, P. Sidelov, I. Pleskun, and O. Lozynska, "Automatic machine learning algorithms for fraud detection in digital payment systems," Eastern-European Journal of Enterprise Technologies, vol. 5, no. 9 (107), pp. 14–26, Oct. 2020.
R. B. Sulaiman, V. Schetinin, and P. Sant, "Review of Machine Learning Approach on Credit Card Fraud Detection," Human-Centric Intelligent Systems, vol. 2, no. 1, pp. 55–68, Jun. 2022.
P. K. Sadineni, "Detection of Fraudulent Transactions in Credit Card using Machine Learning Algorithms," in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Palladam, India, Oct. 2020, pp. 659–660.
V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection using Machine Learning Algorithms," Procedia Computer Science, vol. 165, pp. 631–641, Jan. 2019.
N. Baisholan, M. Turdalyuly, S. Gnatyuk, and K. Kubayev, "Implementation of Machine Learning Techniques To Detect Fraudulent Credit Card Transactions on a Designed Dataset," Journal of Theoretical and Applied Information Technology (JATIT), vol. 101, no. 13, pp. 5279–5287, Jul. 2023.
V. Plotnikova, M. Dumas, and F. P. Milani, "Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements," Data & Knowledge Engineering, vol. 139, May 2022, Art. no. 102013.
C. El Morr, M. Jammal, H. Ali-Hassan, and W. EI-Hallak, Machine Learning for Practical Decision Making: A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics, vol. 334. Cham, Switzerland: Springer International Publishing, 2022.
N. Axford, "Logistic regression," in Exploring Concepts of Child Well-Being: Implications for Children’s Services. Bristol, UK: Policy Press, 2008, pp. 209–212.
M. Loukili, F. Messaoudi, and M. El Ghazi, "Machine learning based recommender system for e-commerce," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 12, no. 4, pp. 1803–1811, Dec. 2023.
T. Andi and E. Utami, "Association rule algorithm with FP growth for book search," in IOP Conference Series: Materials Science and Engineering, Bandung, Indonesia, Apr. 2018, vol. 434.
P. Cunningham and S. J. Delany, "k-Nearest Neighbour Classifiers - A Tutorial," ACM Computing Surveys, vol. 54, no. 6, pp. 1–25, Jul. 2021, Art. no. 128.
E. Altman, A. Nitsure, and Y. Mroueh, “Credit Card Transactions.” Kaggle, [Online]. Available: https://www.kaggle.com/datasets/ealtman2019/credit-card-transactions.
N. Rtayli and N. Enneya, "Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization," Journal of Information Security and Applications, vol. 55, Dec. 2020, Art. no. 102596.
A. Hemmati, H. Nasiri, M. A. Haeri, and M. M. Ebadzadeh, "A Novel Correlation-Based CUR Matrix Decomposition Method," in 2020 6th International Conference on Web Research (ICWR), Tehran, Iran, Apr. 2020, pp. 172–176.
D. S. Sisodia, N. K. Reddy, and S. Bhandari, "Performance evaluation of class balancing techniques for credit card fraud detection," in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, Sep. 2017, pp. 2747–2752.
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, pp. 1–17, Feb. 2022, Art. no. 24.
J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, "A comparison of random forest variable selection methods for classification prediction modeling," Expert Systems with Applications, vol. 134, pp. 93–101, Nov. 2019.
S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, "Random forest for credit card fraud detection," in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China, Mar. 2018, pp. 1–6.
J. V. S. Gollapalli, S. Kalambele, A. Jain, and E. Ariwa, "Emerging Trends of AI and Digital Transactions Replacing Plastic Money in India," in The Business of the Metaverse, 1st ed. New York, NY, USA: Productivity Press, 2023.
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Copyright (c) 2024 Paiboon Manorom, Umawadee Detthamrong, Wirapong Chansanam
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