Author(s)

Zhixin Tang ¹

Affiliation(s)

¹ School of Economics, Guangdong University of Technology, Guangzhou, Guangdong, China

Corresponding Author

Zhixin Tang

ABSTRACT

Both the actual theft of a credit card and the deletion of private credit card data are considered forms of credit card fraud. For detection, there are numerous machine learning algorithms accessible. So, several algorithms that can be used to categorize transactions as fraudulent or lawful are illustrated in this study. In this experiment, the credit card fraud prediction dataset was utilized. The dataset is extremely skewed, hence undersampling is used rather than oversampling. The dataset is separated into test and training data portions, and feature selection is made. The experiment uses the methods of Logistic Regression, Random Forest, SVM, ADABoost, XGBoost, and LightGBM. Moreover, the SMOTE and Optuna's hyperparameter tweaking ways provide model customization. The findings suggest that specific algorithms may be capable of accurately recognizing credit card fraud.

KEYWORDS

Optuna, LightGBM, Fraud detection, Hyperparameter tuning, Machine learning

CITE THIS PAPER

Zhixin Tang. Assessing the feasibility of machine learning-based modelling and prediction of credit fraud outcomes using hyperparameter tuning. Advances in Computer, Signals and Systems (2023) Vol. 7: 84-92. DOI: http://dx.doi.org/10.23977/acss.2023.070212.

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