Construct credit scoring models using logistic regression, neural network and the hybrid model

Abstract

Viet Nam economy is facing many difficulties, the operation of enterprises is not effective leading to the non performing loan ratio of Banks increases. In the period 2007 to 2014, Viet Nam have seen a downtrend in credit growth from 53,89% in 2007 to 11,8% in 2014 without signs of strong recovery in the next period. A decline of credit growth implies that enterprises are facing difficult in approaching credit from lending institutions and those enterprises which operate mainly base on credit will be strongest affected ones. Non performing loan ratio of Banks in Viet Nam has increased in 2007 to 2014, from 2% in 2007 then reached 3,25% in 2014 (highest in 2012 at 4,08%). In this period, almost enterprises could not approach Banks’ loans while Banks are afraid of non performing loan ratio increasing. However, Banks are competing strongly with domestic and foreign ones to achieve shares and maintain profit at the current. Viet Nam is known as a densely populated country (a market size of 90 million people and high proportion of young people) which is considered as a potential retail market for Banks to expand and develop in the next period. To increase the competitiveness of Banks and also improve effective loan risk management, this study applied different methods that are common used to build up credit scoring model such as logistic regression, neural network and hybrid model. Credit scoring model is considered as an application which is developed and widely applied in the sector of finance and banking in the last decades, it is useful in accelerating credit analysis process of Banks. Final results confirmed that characteristics like age, education, marital status, current living status, living time in the current place, type of job, working time in current job, working time in current field, number of dependent people, historical payment have a statistically significant effect on repayment capacity of a customer. Credit scoring models can classify customers according to different strategic purposes of users. And the performance of hybrid models seemed better and more reliable than separate ones.