Credit Scoring System Using Machine Learning

Authors

  • Abhishek Kumar Department of Computer Science Engineering (AI & ML), Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India Author
  • Abhijeet Kumar Department of Computer Science Engineering (AI & ML), Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India Author
  • Aditya Kumar Singh Department of Computer Science Engineering (AI & ML), Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India Author
  • Ms. Nikita Department of Computer Science Engineering (AI & ML), Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2410334

Keywords:

Machine Learning, Credit Scoring System, Decision Making Process

Abstract

This research paper offers a comprehensive examination of credit scoring systems. We will explore the current methodologies employed, the challenges encountered, and potential avenues for improvement. Credit scoring plays a critical role in financial decision-making, impacting both individual access to credit and lender risk management strategies. The paper will analyze traditional credit scoring models alongside emerging trends designed to enhance the accuracy and fairness of credit assessments. Additionally, we will discuss the ethical considerations surrounding credit scoring and propose recommendations for future advancements.

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References

Altman, E. (2008). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

“Brown, L., & Nakamura, M. (2010). Using nontraditional data sources for financial accounts: Possibilities and pitfalls. Economic Perspectives, 34(3), 32-53.

Chen, R., & Rudin, C. (2018). An empirical study of algorithmic discrimination and interpretability. arXiv preprint arXiv:1606.08813.

Lando, R. A. (2014). Credit scoring and its applications. SIAM Review, 46(4), 478-501.

Lusardi, A. (2019). Personal finance and policy challenges: A research agenda. Journal of Economic Literature, 57(4), 110-144.

Moody's Analytics. (2020). Credit Scoring: A Brief Guide. Retrieved from https://www.moodysanalytics.com/-/media/article/2020/credit-scoring-guide.pdf

Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. DOI: https://doi.org/10.1257/jep.31.2.87

Swan, M. (2015). Blockchain: blueprint for a new economy. O'Reilly Media, Inc.

Thomas, L. C. (2000). A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149-172. DOI: https://doi.org/10.1016/S0169-2070(00)00034-0

Tucker, C., Zhang, J., & Mao, H. (2019). Big data and consumer credit in the retail banking sector: An exploration in fintech. International Journal of Financial Studies, 7(1), 11.

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Published

28-05-2024

Issue

Section

Research Articles

How to Cite

[1]
Abhishek Kumar, Abhijeet Kumar, Aditya Kumar Singh, and Ms. Nikita, “Credit Scoring System Using Machine Learning”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 3, pp. 376–380, May 2024, doi: 10.32628/CSEIT2410334.

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