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%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
India Non-Performing Loans, in CEIC report 2018.
https://mygreatlakes.org/educate/knowledge-center/credit.html
King et al., eds., in ICONIP 2006, Part III, LNCS 4234 (2006), pp. 420–429
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
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
L. Nanni, A. Lumini, An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Syst. Applications 36, 3028–3033 (2009)
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
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
J. Simha, Evaluation of feature selection methods for predictive modeling using neural networks in credits scoring (2020).
https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval)
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
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)
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
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
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
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
DOI: https://doi.org/10.1007/978-981-33-4859-2_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4858-5
Online ISBN: 978-981-33-4859-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)