A Study On Loan Default Prediction at Bajaj Finance Limited

References

[1] Agarwal, S., & Hauswald, R. (2010). Distance and private information in lending. The Review of Financial Studies, 23(7), 2757–2788.

[2] Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.

[3] Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The evolution of Fintech: A new post-crisis paradigm? Georgetown Journal of International Law, 47, 1271–1319.

[4] Baesens, B. (2014). Analytics in a big data world. Wiley.

[5] Basel Committee on Banking Supervision. (2004). International convergence of capital measurement and capital standards. Bank for International Settlements.

[6] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

[7] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[8] Crook, J., Edelman, D., & Thomas, L. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.

[9] Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

[10] Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring. Journal of the Royal Statistical Society, 160(3), 523–541.

[11] Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. Wiley.

[12] Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.

[13] Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques. European Journal of Operational Research, 180(1), 1–28.

[14] Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research, 247(1), 124–136.

[15] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.

[16] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

[17] Malhotra, N. K. (2017). Marketing research: An applied orientation. Pearson Education.

[18] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[19] Reserve Bank of India. (2021). Report on trend and progress of banking in India.

[20] Rajput, B., & Gupta, R. (2019). Credit risk analysis in the Indian NBFC sector. International Journal of Financial Studies, 8(2), 45–59.

[21] Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students. Pearson.

[22] Siddiqi, N. (2012). Credit risk scorecards: Developing and implementing intelligent credit scoring. Wiley.

[23] Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit scoring and its applications. SIAM.

[24] Vijaya, B., & Nivetha, P. (2021). Loan default prediction using machine learning techniques. International Journal of Data Science and Analytics, 5(1), 15–24.

[25] Zhang, D., Zhou, X., & Leung, H. (2010). Credit scoring using support vector machines. Decision Support Systems, 44(4), 842–852.

@article{1720272,
      author = {Dr. Chaitra K S, Kiran G N},
      title = {A Study On Loan Default Prediction at Bajaj Finance Limited},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {76-85},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720272.pdf},
      abstract = {Loan default remains one of the more persistent challenges facing non-banking financial companies, and Bajaj Finance Limited, as one of India's largest NBFCs, is no exception. This article examines the factors that shape borrowers' likelihood of default and the analytical approach through which such risk can be anticipated. Drawing on a structured survey of 101 respondents together with an underlying logistic regression framework, the study looks at how income level, EMI burden, existing liabilities, credit score, repayment history and exposure to unexpected expenses relate to repayment outcomes. It also outlines a probability-based classification scheme that sorts borrowers into low-, medium- and high-risk categories, a tool intended to support faster and more consistent credit decisions. The survey results show that respondents are drawn predominantly from younger, lower- and middle-income segments, that a substantial share have experienced delayed EMI payments at some point, and that poor financial planning, existing debt obligations and unexpected expenses are widely seen as significant contributors to repayment difficulty. Credit score and repayment history, meanwhile, are broadly recognised as central to loan approval and to predicting future behaviour. The article concludes that combining sound credit assessment with continuous account monitoring and predictive analytics allows lenders such as Bajaj Finance to identify risk earlier and manage it more effectively, supporting growth without compromising portfolio quality.},
      keywords = {Loan Default, Credit Risk, Bajaj Finance, NBFC, Logistic Regression, Risk Classification, Repayment Behaviour},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1720272}
  }