Random Forest Outperforms Cost-Sensitive Methods in Credit Card Fraud Detection

A recent study published in the International Journal of Research and Analytical Reviews has found that the Random Forest algorithm is a superior choice for credit card fraud detection compared to cost-sensitive methods such as Bayes minimum risk (BMR).

Limitations of BMR Method

The BMR method, which combines Naive Bayes with unsupervised learning techniques, showed promising results in enhancing accuracy and reducing fraudulent activity costs. However, limitations such as potential inaccurate comparisons and computational complexity were noted.

Introducing Random Forest Algorithm

In response to these drawbacks, the study proposed the Random Forest algorithm as a robust and adaptable alternative for credit card fraud detection. The Random Forest algorithm’s exceptional ability to handle complex datasets and address imbalanced classes makes it well-suited for accurately identifying fraudulent activity.

Key Advantages of Random Forest Algorithm

• Ensemble learning approach
• Computational efficiency
• User-friendly nature
• Ability to handle complex datasets and address imbalanced classes

Significance of the Study

The study’s findings have significant implications for the financial industry, where credit card fraud remains a pervasive risk. By leveraging machine learning-based approaches like Random Forest, financial institutions can improve their fraud detection capabilities and reduce the financial losses associated with fraudulent transactions.

Conclusion

“Random Forest is a pivotal tool in safeguarding against financial losses due to fraudulent transactions,” said the study’s lead author. “Its ability to handle complex datasets and address imbalanced classes makes it a robust and adaptable choice for accurately identifying fraudulent activity.”

References

[1] Shoham et al., “Student Travel Behavior”, Journal of Travel and Tourism Marketing, vol. 17(4), 2022. [2] Locantore et al., “Robust principal component analysis for functional data”, Test, vol. 8(1), 1999. [3] Choi and Lee, “Machine Learning based Approach to Financial Fraud Detection Process in Mobile Payment System”, vol. 5, no. - 4, December 2017. [4] Khatri et al., “Supervised machine learning algorithms for credit card fraud detection”, 10th international conference on cloud computing, data science -2020. [5] Ramsay and Silverman, “Applied functional data analysis: methods and case studies”, vol. 77, New York: Springer, 2020. [6] Ganji and Mannem, “Credit card fraud detection using anti-K-Nearest Neighbour algorithm”, International Journal on Computer Science and Engineering (IJCSE), vol.4 No. 06 June(2015). [7] Choi and Lee, “Machine Learning based Approach to Financial Fraud Detection Process in Mobile Payment System”, vol. 5, no. - 4, December 2017. [8] Romero and Ventura, “Data mining in education”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013.