Financial Fraud Detection using Machine Learning : Credit Card Fraud

Financial Fraud Detection using Machine Learning : Credit Card Fraud

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2023 by IJRES Journal
Volume-10 Issue-3
Year of Publication : 2023
Authors : Odeajo Israel, Akinmoluwa Oluseye, Sharon Ojo, Otesanya Temitope Deborah
DOI : 10.14445/23497157/IJRES-V10I3P104

How to Cite?

Odeajo Israel, Akinmoluwa Oluseye, Sharon Ojo, Otesanya Temitope Deborah, "Financial Fraud Detection using Machine Learning : Credit Card Fraud," International Journal of Recent Engineering Science, vol. 10, no. 3, pp. 23-32, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V10I3P104

Abstract
Instances of credit card fraud occur with great frequency and often lead to serious financial losses. The volume of online transactions has experienced significant growth, with a substantial proportion of those transactions being attributed to credit card transactions made online. Hence, credit card fraud detection applications are highly valued and in demand by banking institutions and financial institutions. Fraudulent transactions can manifest in diverse forms and can be classified into distinct categories. This research centers on cases of fraudulent activity from open-source data from kaggle.com. Fraudulent activities are examined by employing a sequence of machine learning models, and the optimal approach is determined through an extensive analysis process. We used three algorithms, namely the random forest algorithm, the Decision Tree classifier algorithm, linear regression and three sampling techniques in order to balance the dataset. We also used twelve (12) different models for the prediction of credit card fraud. The evaluation offers a comprehensive guide for the selection of an ideal algorithm based on the nature of fraudulent activities. Additionally, we demonstrate the evaluation process using a suitable metric for performance measurement. The twelve models were compared, and the best model, with an accuracy of 97.4%, was a Random Forest Classifier developed using the SMOTE sampling technique after hyperparameter tuning.

Keywords
Algorithm, Credit card fraud, Decision tree, Fraudulent transactions, Random forest.

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