Predicting Educational Loan Defaults: Application of Artificial Intelligence Models

40 Pages Posted: 27 Dec 2019

See all articles by M Jayadev

M Jayadev

Indian Institute of Management (IIMB), Bangalore

Neel Shah

Columbia Business School

Ravi Vadlamani

Institute for Development and Research in Banking Technologies

Date Written: December 20, 2019

Abstract

We show that Educational loans is a case for application of artificial intelligence models to predict potential defaulters with a reasonable accuracy. Ensemble models tend to perform better than simple artificial techniques and statistical models and that the performance can be improved significantly by model stacking. We argue here that a stacked model created using a few sparsely correlated base models is likely to be the best model for predicting Educational loan defaults given that the interaction between diverse features would create non-linearities that are impossible to model using a single model, there is little a priori knowledge of the distribution of educational loan defaults and the relationships between various factors that govern the distribution. It is evident that collateral-free loans have a considerably higher rate of default with moral hazard problem as compared to the loans with collateral. Students qualifying from well rated educational institutions are prone to strategic default or wilful default. Considering the impact of macroeconomic conditions greatly improve the classification accuracies.

Keywords: Credit Risk, Educational Loans, Statistical Techniques, Artificial Intelligence Techniques

Suggested Citation

Jayadev, M and Shah, Neel and Vadlamani, Ravi, Predicting Educational Loan Defaults: Application of Artificial Intelligence Models (December 20, 2019). IIM Bangalore Research Paper No. 601 (2019), Available at SSRN: https://ssrn.com/abstract=3510012 or http://dx.doi.org/10.2139/ssrn.3510012

M Jayadev (Contact Author)

Indian Institute of Management (IIMB), Bangalore

Bannerghatta Road
Bangalore, Karnataka 560076
India

Neel Shah

Columbia Business School ( email )

India

Ravi Vadlamani

Institute for Development and Research in Banking Technologies ( email )

Castle Hills, Road No.1
Masab Tank, Hyderabad 500 057
India

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