No abstract

The deep-learning model outperforms the conventional structured models developed by using econometric techniques. Instead, econometric techniques provide an important insight into specific factors and their contribution to default probability. Using data from an Armenian universal credit organization that contains financial and nonfinancial variables of more than 9,000 borrowers of agriculture loans from 2012 to 2017 years, we compare deep neural networks' performance against conventional and widely used econometric techniques. Delays on past loans, together with loan size and currency, are major factors contributing to loan default probability prediction accuracy. The set of statistically significant or important variables differs between econometric and deep learning models proving that the latter can capture nonlinear relationships.