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Financial Analytics: Science and Experience
 

Adaptation of models predicting customers' creditworthiness in line with new incoming data on clients

Vol. 10, Iss. 6, JUNE 2017

PDF  Article PDF Version

Received: 20 December 2016

Received in revised form: 17 March 2017

Accepted: 24 March 2017

Available online: 15 June 2017

Subject Heading: MONITORING AND PREDICTION OF BANKING RISKS

JEL Classification: C02, C53, G21

Pages: 663-674

https://doi.org/10.24891/fa.10.6.663

Krasheninnikov V.R. Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
kvrulstu@mail.ru

Klyachkin V.N. Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
v_kl@mail.ru

Shunina Yu.S. JSC Ulyanovsk Instrument Manufacturing Design Bureau, Ulyanovsk, Russian Federation
mamaeva_29_12@mail.ru

Importance Machine learning methods are used to predict repayment of loans by borrowers. It is an acute task to update the structure of the aggregate forecast after some time in order to adapt to new clients' characteristics as well as to provide sufficient prediction accuracy.
Objectives The aim is to provide the structure updating of the aggregate forecasting method to improve the prediction accuracy.
Methods In this paper, we used the machine learning methods with different classifiers based on a neural network, logistic regression, discriminant analysis, naive Bayes classifier, Support Vector Machines, etc. To adjust parameters of the models, pseudo-gradient procedure is used. The quality of the model structure obtained with updated parameters is assessed by the average square error in the control sample.
Results We developed a model structure updating method for forecasting customers' loan repayment, the effectiveness of which has been confirmed by practical tests based on the data according to Russian borrowers.
Conclusions and Relevance Using the pseudo-gradient procedure of adjusting the parameters of the chosen model provides an accurate prediction for a certain period of time. However, after significant changes in the credit situation, this model is not able to provide a sufficient accuracy of forecast for any values of its parameters. Therefore, from time to time it is needed to change the structure of the model to provide sufficient prediction accuracy to get more justified decisions about granting credits to new customers on the new terms. The proposed method can be applied in automated systems to support decision-making about granting loans in the banking sector.

Keywords: loan repayment, forecasting approach, machine learning, pseudo-gradient procedure, model structure

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