ICTACT Journal on Soft Computing

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Enhancing Credit Card Fraud Detection in Financial Transactions Through Improved Random Forest Algorithm


Affiliations
1 Department of Computer Science, S.I.V.E.T. College, India
     

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Credit card Fraud detection is a critical task in various industries, including finance and e-commerce, where identifying fraudulent activities can help prevent financial losses and protect users. It begins by combining two datasets containing fraudulent and non-fraudulent transactions to create a comprehensive dataset for analysis. Data is preprocessed by removing unnecessary features, calculating distance metrics, and generating new variables to capture temporal patterns and transaction history. Multicollinearity issues are addressed through feature selection. Improved Random Forest (RF) algorithm is used to improve fraud detection. The experimental results indicate that the improved Random Forest algorithm achieves commendable accuracy in fraud detection. The proposed model achieves 99.87% training accuracy and 99.41% testing accuracy. The Model’s performance is evaluated by measuring precision, recall, F1-score and support. Our research emphasizes the importance of considering improved algorithms to achieve better results. The findings provide valuable insights for organizations aiming to enhance their fraud detection capabilities and make informed decisions to protect their systems and users.

Keywords

Credit Card, Fraud detection, Random Forest, Classification, Accuracy, Precision, Recall, and F1 Score.
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  • Enhancing Credit Card Fraud Detection in Financial Transactions Through Improved Random Forest Algorithm

Abstract Views: 124  |  PDF Views: 2

Authors

B. Sowmiya
Department of Computer Science, S.I.V.E.T. College, India

Abstract


Credit card Fraud detection is a critical task in various industries, including finance and e-commerce, where identifying fraudulent activities can help prevent financial losses and protect users. It begins by combining two datasets containing fraudulent and non-fraudulent transactions to create a comprehensive dataset for analysis. Data is preprocessed by removing unnecessary features, calculating distance metrics, and generating new variables to capture temporal patterns and transaction history. Multicollinearity issues are addressed through feature selection. Improved Random Forest (RF) algorithm is used to improve fraud detection. The experimental results indicate that the improved Random Forest algorithm achieves commendable accuracy in fraud detection. The proposed model achieves 99.87% training accuracy and 99.41% testing accuracy. The Model’s performance is evaluated by measuring precision, recall, F1-score and support. Our research emphasizes the importance of considering improved algorithms to achieve better results. The findings provide valuable insights for organizations aiming to enhance their fraud detection capabilities and make informed decisions to protect their systems and users.

Keywords


Credit Card, Fraud detection, Random Forest, Classification, Accuracy, Precision, Recall, and F1 Score.

References