Skip to main content

Credit Score Prediction Using Machine Learning

  • Conference paper
  • First Online:
Machine Learning and Information Processing

Abstract

A strong financial and economic status of a country is necessary for the well-being of all the citizens. A big part of the financial ecosystem is the banking system. They help the economy to grow by lending loans to corporations and individuals who use this money by investing in some enterprise or business. This cash flow is essential in any healthy economy. Consequently, the unpaid or nonperforming loans put stress on economy. To deal with this situation, Banks and credit card companies estimate credit score. This score provides an idea regarding the lender’s ability to make the repayment. To facilitate and improve the credit score prediction, we worked on a number of algorithms like linear regression, logistic regression and K-Nearest Neighbor algorithm (KNN). Using KNN algorithm along with some statistical work on dataset, we were able to obtain a very healthy accuracy of 89%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. H.A. Abdou, J. Pointon, Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell. Syst. Account. Finance Manage. 18(2–3)

    Google Scholar 

  2. A.F. Atiya, Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans. Neural Networks 12(4), 929–935 (2001)

    Article  Google Scholar 

  3. India Non-Performing Loans, in CEIC report 2018.

    Google Scholar 

  4. https://www.investopedia.com/terms/c/credit-worthiness.asp

  5. https://mygreatlakes.org/educate/knowledge-center/credit.html

  6. King et al., eds., in ICONIP 2006, Part III, LNCS 4234 (2006), pp. 420–429

    Google Scholar 

  7. A. Motwani, G. Bajaj, S. Mohane, Predictive modelling for credit risk detection using ensemble method. Int. J. Comput. Sci. Engineering. 6. 863–867. https://doi.org/10.26438/ijcse/v6i6.863867

  8. N.-C. Hsieh, L.-P. Hung, A Data Driven Ensemble Classifier for Credit Scoring Analysis. Expert Syst. Appl. 37, 534–545 (2010). https://doi.org/10.1016/j.eswa.2009.05.059

    Article  Google Scholar 

  9. L. Nanni, A. Lumini, An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Syst. Applications 36, 3028–3033 (2009)

    Article  Google Scholar 

  10. R.E. Turkson, E.Y. Baagyere, G.E. Wenya, A machine learning approach for predicting bank credit worthiness, in 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), Lodz (2016), pp. 1–7

    Google Scholar 

  11. C.R.D. Devi, R.M. Chezian, A relative evaluation of the performance of ensemble learning in credit scoring, in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore (2016), pp. 161–165

    Google Scholar 

  12. J. Simha, Evaluation of feature selection methods for predictive modeling using neural networks in credits scoring (2020).

    Google Scholar 

  13. https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval)

  14. A. Moldagulova, R.B. Sulaiman, Using KNN algorithm for classification of textual documents, in 2017 8th International Conference on Information Technology (ICIT), Amman (2017), pp. 665–671

    Google Scholar 

  15. I. Dokmanic, R. Parhizkar, J. Ranieri, M. Vetterli, Euclidean Distance Matrices: essential theory, algorithms, and applications. IEEE Signal Process. Mag. 32(6), 12–30 (2015)

    Article  Google Scholar 

  16. https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1531424125/KNN_final1_ibdm8a.png

  17. X. Yu, X. yu, The research on an adaptive k-nearest neighbors classifier, in 2006 5th IEEE International Conference on Cognitive Informatics, Beijing (2006), pp. 535–540

    Google Scholar 

  18. C.L. Castro, A.P. Braga, Optimization of the area under the ROC curve, in 2008 10th Brazilian Symposium on Neural Networks, Salvador (2008), pp. 141–146

    Google Scholar 

  19. D. Swain, S. Pani, D. Swain, Diagnosis of coronary artery disease using 1-D convolutional neural network. Int. J. Rec. Technol. Eng. 8(2), 2959–2966 (2019)

    Google Scholar 

  20. D. Swain, S. Pani, D. Swain, An efficient system for the prediction of coronary artery disease using dense neural network with hyper parameter tuning. Int. J. Innov. Technol. Exploring Eng. 8(6S) (2019), pp. 689–695

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Debabrata Swain .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Swain, D., Agrawal, R., Chandak, A., Lapshetwar, V., Chandak, N., Vaswani, A. (2021). Credit Score Prediction Using Machine Learning. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_7

Download citation

Publish with us

Policies and ethics