ENHANCING CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

Abstract:

Credit card fraud has become a significant concern for financial institutions and cardholders worldwide. With the increasing sophistication of fraudsters, traditional fraud detection methods are often inadequate in identifying fraudulent transactions accurately. In recent years, ensemble learning techniques have gained attention in various domains due to their ability to improve prediction accuracy by combining the strengths of multiple models. This abstract presents a study focused on enhancing credit card fraud detection using ensemble learning techniques.

The study explores the application of ensemble learning methods, such as Random Forest, Gradient Boosting, and AdaBoost, to improve the accuracy and robustness of credit card fraud detection systems. The ensemble models are trained on a comprehensive dataset containing labeled credit card transactions, including both genuine and fraudulent cases. Feature engineering techniques are applied to extract relevant information from the transaction data, enabling the models to capture patterns and anomalies associated with fraudulent activities.

The experiment evaluates the performance of individual models and compares it with ensemble models. Evaluation metrics, such as precision, recall, and F1-score, are employed to assess the effectiveness of each approach in identifying fraudulent transactions accurately while minimizing false positives. Additionally, the study investigates the impact of varying ensemble sizes and composition on the overall performance of the models.

The results demonstrate that ensemble learning techniques outperform individual models, showcasing their ability to improve credit card fraud detection accuracy. The ensemble models exhibit higher precision, recall, and F1-score, indicating their capability to identify fraudulent transactions while minimizing false positives. The study also reveals that the optimal ensemble size and composition depend on the characteristics of the dataset, with different techniques offering varying performance benefits.

In conclusion, this study highlights the potential of ensemble learning techniques in enhancing credit card fraud detection. By leveraging the power of multiple models, these techniques can effectively identify fraudulent transactions, providing financial institutions and cardholders with improved security and protection against fraudulent activities. The findings of this research contribute to the ongoing efforts to develop robust fraud detection systems and mitigate the financial losses associated with credit card fraud. Future work could explore the integration of additional machine learning algorithms and data sources to further enhance the performance of ensemble models in detecting credit card fraud.

ENHANCING CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES, GET MORE MASTERS COMPUTER SCIENCE

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