COMPARATIVE ANALYSIS OF TRADITIONAL STATISTICAL MODELS AND MACHINE LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION

Abstract:

Credit card fraud is a prevalent and costly problem that affects financial institutions and consumers worldwide. Detecting fraudulent transactions accurately and efficiently is crucial for minimizing financial losses and protecting customers from unauthorized activities. In recent years, both traditional statistical models and machine learning models have been widely employed for credit card fraud detection.

This study aims to provide a comparative analysis of traditional statistical models and machine learning models in the context of credit card fraud detection. The research investigates the effectiveness, efficiency, and practicality of these two approaches by evaluating their performance on real-world datasets.

Traditional statistical models, such as logistic regression, decision trees, and rule-based systems, have been extensively used in fraud detection. These models rely on predefined rules and assumptions to identify fraudulent transactions. They often require domain expertise to design and implement the rules effectively. On the other hand, machine learning models, including neural networks, support vector machines, and random forests, have gained popularity due to their ability to learn patterns and detect complex fraud patterns automatically.

To conduct the comparative analysis, this study collects a comprehensive dataset containing a mix of legitimate and fraudulent credit card transactions. The dataset is preprocessed to ensure consistency and eliminate noise. Both traditional statistical models and machine learning models are trained on the dataset and evaluated using various performance metrics, such as accuracy, precision, recall, and F1-score.

The results of the comparative analysis indicate that machine learning models generally outperform traditional statistical models in terms of overall fraud detection accuracy. Machine learning models can capture intricate patterns and adapt to changing fraud patterns effectively. However, traditional statistical models exhibit superior interpretability and require less computational resources, making them more suitable for scenarios where explainability and computational efficiency are crucial.

This research provides insights into the strengths and limitations of traditional statistical models and machine learning models for credit card fraud detection. The findings can help financial institutions and researchers make informed decisions about selecting appropriate models for their specific needs. Moreover, this study highlights the importance of continuous model evaluation and adaptation to stay ahead of evolving fraud techniques in the dynamic financial landscape.

COMPARATIVE ANALYSIS OF TRADITIONAL STATISTICAL MODELS AND MACHINE LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION, GET MORE MASTERS COMPUTER SCIENCE 

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