ANALYSIS OF MACHINE LEARNING METHODS PERFORMANCE IN TRANSACTIONS OF CREDIT CARD FRAUD IN ONLINE BANKING
DOI:
https://doi.org/10.22159/ijet.2025v13.57902Keywords:
Credit card fraud, Online banking, Machine learning, Digital payments, Fraud transactions, Deep learning, Predictive analyticsAbstract
In Online Banking, fraud is increasing day by day. The different types of fraud include data breaches, unauthorized access, and authentication-like attacks. The conventional system in which password password-based, has declined in reliability, achieving only moderate performance to enhance to detect fraud. Online banking credit card fraud (CCF) is the basis of this research. The research adhered to the processing steps, which included data cleansing (which involved dealing with missing information and deleting duplicates) and standardization, using CCF data. The following step is label encoding, which enables the model to perform mathematical calculations. Then, it uses IHT to deal with data that contains imbalanced classes. The k-fold cross-validation method then splits the data in half. Multilayer Perceptrons and K-Nearest Neighbors are also part of the Ensemble model, which also includes Decision Trees and Random Forest. Every model in the ensemble contributes something useful, which boosts the overall performance. Outperforming LSTM, GBM, and neural network models, the ensemble model attained the highest accuracy (ACC) of 99.99%. The power of the ensemble model lies in its capacity to integrate numerous models. The results of the model demonstrate its top performance, scalability, and dependability.
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