FRAUD DETECTION IN BANKING DATA BY MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.5281/zenodo.21408930Keywords:
Fraud Detection, Banking System, “ML and DL Models like Light GBM, XG Boost, Cat Boost, Neural Network and Hybrid model like LG + XG+ CAT, LG + XG, LG + CAT, XG + CAT, Artificial Intelligence, APIs Drivers, Web browser, Processor, RAM, HDDAbstract
Credit cards emerged as a prevalent payment method due To advance technological advances and expand e-commerce services. This has led to an increase in financial transactions. Furthermore, a significant increase in fraud has resulted in increased financial transaction fees. As a result, fraud research has proven to be a compelling topic. This study examines the use of class weight hyperparameters to adapt the weights of real and fraudulent transactions. Because real challenges such as unbalanced data are considered, Bayesian optimization is used to identify the optimal hyperparameter. We recommend Weight Voices as an initial measure of unbalanced data. DL is used to optimize the proposed method of hyperparameters, especially the weight division, to further improve performance. Empirical data are used to evaluate proposed strategies in the experimental environment. To handle imbalanced data records more effectively, “we use recall metrics in addition to traditional ROC-AUC”. Evaluate Cat Boost, Light GBM, XG Boost, and logistic regression separately using five cross-validation techniques. The performance of the combined algorithm is evaluated using the majority voice ensemble learning method. The results reveal that the recommended strategies work better than the most advanced ones and make a big difference in performance.
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