Abstract
As the usage of credit cards has become more common in health care applications of everyday life, banks have found it very difficult to detect credit card fraud (CCF) systematically. The fraudulent activities should be identified and detected using new techniques. As a result, machine learning (ML) can help detect CCF in transactions while reducing the strain on financial institutions. This research aims to improve cybersecurity measures by detecting fraudulent transactions in datasets. The new classifier strategies cluster and classifier-based decision tree (CCDT), cluster and classifier-based logistic regression (CCLR), and cluster and classifier-based random forest (CCRF) are modeled in this research. The proposed strategies are applied to detect fraudulent health care activities. This research performed the preprocessing through the feature extraction, sampling, and transformation stages, and the proposed classifiers are simulated, and the results are analyzed. The significant results expected range of the proposed classifiers over the other methods are accuracy—(99.95%, 99.97%), precision—(99.96%, 99.98%), sensitivity—(99.9%, 100%), specificity—(99.8%, 100%). The parameters \(\mu \), location, the binary variable, cluster size, and decision tree sampling observations affect the classifiers' performance. CCRF and CCLR obtain the expected significant results than other existing methods.
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References
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
Alfaiz NS, Fati SM (2022) Enhanced credit card fraud detection model using machine learning. Electronics 11:662. https://doi.org/10.3390/electronics11040662
Belmonte JL, Segura-Robles A, Moreno-Guerrero A-J, Parra-González ME (2020) Machine learning and big data in the impact literature. A bibliometric review with scientific mapping in web of science. Symmetry 12:495
Benchaji I, Douzi S, El Ouahidi B et al (2021) Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J Big Data 8:151. https://doi.org/10.1186/s40537-021-00541-8
Bhaskaran S, Marappan R (2021) Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00509-4
Carneiro EM, Forster CHQ, Mialaret LFS, Dias LAV, da Cunha AM (2022) High-cardinality categorical attributes and credit card fraud detection. Mathematics 10:3808. https://doi.org/10.3390/math10203808
Dalal S, Seth B, Radulescu M, Secara C, Tolea C (2022) Predicting fraud in financial payment services through optimized hyper-parameter-tuned XGBoost model. Mathematics 10:4679. https://doi.org/10.3390/math10244679
Dang TK, Tran TC, Tuan LM, Tiep MV (2021) Machine learning based on resampling approaches and deep reinforcement learning for credit card fraud detection systems. Appl Sci 11:10004. https://doi.org/10.3390/app112110004
Esenogho E, Mienye ID, Swart TG, Aruleba K, Obaido G (2022) A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access 10:16400–16407. https://doi.org/10.1109/ACCESS.2022.3148298
Fatima EB, Omar B, Abdelmajid EM, Rustam F, Mehmood A, Choi GS (2021) Minimizing the overlapping degree to improve class-imbalanced learning under sparse feature selection: application to fraud detection. IEEE Access 9:28101–28110
Hoang NL, Trang LH, Dang TK (2020) A comparative study of the some methods used in constructing coresets for clustering large datasets. SN Comput Sci 1:1–12
Jain D, Choudhary D, Anand A, Trivedi NK, Gautam V, Mohapatra SK (2022) Cybersecurity solutions using AI techniques. In: 2022 10th International conference on reliability, Infocom technologies and optimization (trends and future directions) (ICRITO), Noida, India, 2022, pp 1–8. https://doi.org/10.1109/ICRITO56286.2022.9965045
Li Z, Huang M, Liu G, Jiang C (2021) A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection. Expert Syst Appl 175:114750
Lin T-H, Jiang J-R (2021) Credit card fraud detection with autoencoder and probabilistic random forest. Mathematics 9:2683. https://doi.org/10.3390/math9212683
Malik EF, Khaw KW, Belaton B, Wong WP, Chew X (2022) Credit card fraud detection using a new hybrid machine learning architecture. Mathematics 10:1480. https://doi.org/10.3390/math10091480
Marappan R, Sethumadhavan G (2018) Solution to graph coloring using genetic and tabu search procedures. Arab J Sci Eng 43:525–542. https://doi.org/10.1007/s13369-017-2686-9
Marappan R, Sethumadhavan G (2020) Complexity analysis and stochastic convergence of some well-known evolutionary operators for solving graph coloring problem. Mathematics 8:303. https://doi.org/10.3390/math8030303
Marappan R, Sethumadhavan G (2021) Solving graph coloring problem using divide and conquer-based turbulent particle swarm optimization. Arab J Sci Eng. https://doi.org/10.1007/s13369-021-06323-x
Mniai A, Jebari K (2022) Credit card fraud detection by improved SVDD. In: Proceedings of the world congress on engineering 2022. WCE 2022, July 6–8, 2022, London, UK
Sharma M, Sharma H, Bhutani P, Sharma I (2021) Credit card fraud detection using machine learning algorithms. In: Innovations in cyber physical systems. Springer Singapore
Tran TC, Dang TK (2021) Machine learning for prediction of imbalanced data: credit fraud detection. In: Proceedings of the 2021 15th international conference on ubiquitous information management and communication (IMCOM), Seoul, Korea, 4–6 January 2021, pp 1–7
Trivedi NK, Simaiya S, Lilhore UK, Sharma SK (2020) An efficient credit card fraud detection model based on machine learning methods. IJAST 29(05):3414–3424
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Jayanthi, E., Ramesh, T., Kharat, R.S. et al. Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies. Soft Comput 27, 7555–7565 (2023). https://doi.org/10.1007/s00500-023-07954-y
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DOI: https://doi.org/10.1007/s00500-023-07954-y