A COMPARATIVE ANALYSIS OF CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING CLASSIFICATION ALGORITHM

Abstract:

Credit card fraud is a persistent and evolving problem in the financial industry, leading to significant financial losses for both individuals and businesses. Detecting fraudulent transactions in real-time is crucial to mitigate these losses and protect customers. Machine learning algorithms have shown promise in effectively identifying fraudulent activities by analyzing patterns and anomalies in credit card transactions.

This study presents a comparative analysis of machine learning classification algorithms for credit card fraud detection. The aim is to evaluate the performance of different algorithms and identify the most effective approach for detecting fraudulent transactions.

Several popular machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks, are implemented and evaluated using a publicly available credit card fraud dataset. The dataset contains a mixture of genuine and fraudulent transactions, providing a realistic representation of real-world scenarios.

Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the algorithms’ performance in detecting fraudulent transactions. The algorithms are trained, validated, and tested using cross-validation techniques to ensure robustness and generalization.

The experimental results reveal that different machine learning algorithms exhibit varying levels of performance in credit card fraud detection. While some algorithms achieve high accuracy and precision, others excel in recall and F1-score. The study provides insights into the strengths and weaknesses of each algorithm and identifies the most suitable algorithm for credit card fraud detection.

Furthermore, feature selection techniques and ensemble methods are explored to enhance the performance of the chosen algorithm. The impact of feature engineering, including dimensionality reduction and feature scaling, is also investigated to optimize the detection accuracy.

The findings of this study contribute to the existing literature on credit card fraud detection, providing valuable insights into the comparative performance of machine learning classification algorithms. The results can guide financial institutions and researchers in selecting the most effective algorithm for real-time fraud detection, ultimately leading to improved security and reduced financial losses.

Keywords: Credit card fraud detection, machine learning, classification algorithms, performance evaluation, feature selection, feature engineering.

A COMPARATIVE ANALYSIS OF CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING CLASSIFICATION ALGORITHM, GET MORE MASTERS COMPUTER SCIENCE 

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