Abstract
The aim of this research work is to classify credit card fraudulent transactions. Nowadays, online transactions have become a necessary part of our lives. Credit card fraud has skyrocketed. In fact, it is one of the most prevalent menaces of the banking, financial services, and insurance (BFSI) sector. In the era of digitalization, there are so many frauds happening. Credit card fraud is the most common thing happening nowadays; thus, the need to identify the same is necessary. In this work, firstly, collected the unstructured data related to this research from a frequently used site, i.e., Kaggle. Secondly, implemented different machine learning classification algorithms (supervised) like linear regression (LR), decision trees (DT), artificial neural networks (ANN), and gradient boosting (GB). Among all machine learning (ML) algorithms, DT outperforms the other algorithms with accuracy. The performance of these specified algorithms was marked by their comparative analysis with some performance metrics.
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References
Alić B, Gurbeta L, Badnjević A (2017) Machine learning techniques for classification of diabetes and cardiovascular diseases. In: 2017 6th mediterranean conference on embedded computing (MECO), pp 1–4. https://doi.org/10.1109/MECO.2017.7977152
Bonaccorso G (2017) Machine learning algorithms. Packt Publishing Ltd.
Singh A, Singh P, Tiwari AK (2021) A comprehensive survey on machine learning. J Manage Serv Sci 1(1):3
Punia SK, Kumar M, Stephan T, Deverajan GG, Patan R (2021) Performance analysis of machine learning algorithms for big data classification: Ml and AI-based algorithms for big data analysis. Int J E-Health Med Commun (IJEHMC) 12(4):60–75
Tekkali CG, Vijaya J (2021, August) A survey: methodologies used for fraud detection in digital transactions. In: 2021 second international conference on electronics and sustainable communication systems (ICESC). IEEE, pp 1758–1765
Alarfaj FK, Malik I, Khan HU, Almusallam N, Ramzan M, Ahmed M (2022) Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access 10:39700–39715. https://doi.org/10.1109/ACCESS.2022.3166891
Itoo F, Meenakshi, Singh S (2021) Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inf Tecnol 13:1503–1511. https://doi.org/10.1007/s41870-020-00430-y
Khare N, Sait SY (2018) Credit card fraud detection using machine learning models and collating machine learning models. Int J Pure Appl Math 118(20):825–838
Sailusha R, Gnaneswar V, Ramesh R, Rao GR (2020) Credit card fraud detection using machine learning. In: 2020 4th international conference on intelligent computing and control systems (ICICCS), pp 1264–1270. https://doi.org/10.1109/ICICCS48265.2020.9121114
Gracia SVJB, Ponsam JG, Preetha S, Subhiksha J (2021) Payment fraud detection using machine learning techniques. In: 2021 4th international conference on computing and communications technologies (ICCCT), pp 623–626. https://doi.org/10.1109/ICCCT53315.2021.9711887
Moumeni L, Saber M, Slimani I, Elfarissi I, Bougroun Z (2022) Machine learning for credit card fraud detection. In: Bennani S, Lakhrissi Y, Khaissidi G, Mansouri A, Khamlichi Y (eds) WITS 2020. Lecture notes in electrical engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_20
Sharma S, Kataria A, Sandhu JK, Ramkumar KR (2022) Credit card fraud detection using machine and deep learning techniques. In: 2022 3rd international conference for emerging technology (INCET), pp 1–7. https://doi.org/10.1109/INCET54531.2022.9824065
Babenko V, Panchyshyn A, Zomchak L, Nehrey M, Artym-Drohomyretska Z, Lahotskyi T (2021) Classical machine learning methods in economics research: macro and micro level example. WSEAS Trans Bus Econ 18:209–217
Gulati K, Kumar SS, Boddu RSK, Sarvakar K, Sharma DK, Nomani MZM (2022) Comparative analysis of machine learning-based classification models using sentiment classification of tweets related to COVID-19 pandemic. Mater Today Proc 51:38–41
Elbadawi M, Gaisford S, Basit AW (2021) Advanced machine-learning techniques in drug discovery. Drug Discov Today 26(3):769–777
Ghazal TM, Alzoubi HM (2022) Fusion-based supply chain collaboration using machine learning techniques. Intell Autom Soft Comput 31(3):1671–1687
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Tekkali, C.G., Natarajan, K., Guruteja Reddy, T. (2024). Performance Comparison of Various Supervised Learning Algorithms for Credit Card Fraud Detection. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_25
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DOI: https://doi.org/10.1007/978-981-99-7954-7_25
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