INVESTIGATING THE IMPACT OF PREPROCESSING TECHNIQUES ON CREDIT CARD FRAUD DETECTION ACCURACY

Abstract:

Credit card fraud is a significant concern for financial institutions and consumers alike. Detecting fraudulent transactions accurately is crucial for preventing financial losses and protecting customers’ sensitive information. One key aspect of credit card fraud detection is the preprocessing of transaction data before applying machine learning algorithms. This study aims to investigate the impact of various preprocessing techniques on the accuracy of credit card fraud detection.

The research methodology involves collecting a dataset comprising legitimate and fraudulent credit card transactions. The dataset is preprocessed using different techniques, including data cleaning, feature scaling, feature selection, and outlier detection. Several classification algorithms, such as logistic regression, decision trees, random forests, and support vector machines, are applied to the preprocessed data to detect fraudulent transactions.

The performance of each preprocessing technique is evaluated based on metrics such as accuracy, precision, recall, and F1-score. Additionally, the computational cost and time required for each technique are considered. The results of the study will highlight the effectiveness of various preprocessing techniques in improving the accuracy of credit card fraud detection.

The findings of this research will provide valuable insights into the impact of preprocessing techniques on credit card fraud detection accuracy. Financial institutions and researchers can leverage these insights to enhance their fraud detection systems and develop more robust models. By accurately identifying fraudulent transactions, the potential financial losses for both consumers and financial institutions can be minimized, resulting in increased trust and security in the credit card industry.

Keywords: credit card fraud, fraud detection, preprocessing techniques, machine learning, accuracy, classification algorithms

INVESTIGATING THE IMPACT OF PREPROCESSING TECHNIQUES ON CREDIT CARD FRAUD DETECTION ACCURACY, GET MORE MASTERS COMPUTER SCIENCE 

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