By: Nguyen Viet Duc - VNP 21
Supervisor: Dr. Nguyen Thi Thuy Linh
The largest part in Asset of any bank in its balance sheet is loans, which accounted over 70% of total bank’s asset. Therefore loan becomes the biggest factor affecting bank’s profit/loss (PnL) and managing loan becomes the main point in banking management. For the reason that credit portfolio plays important role in bank’s PnL, it is required that banks need to issue and implement policies and techniques to manage risk in every stage of granting loans process.
In the past, credit rating decisions were made by bank’s individual experts (or a team). It seems to be not an efficient way for banks to do so. Together with the growth of banking industry, many statistical methods for credit rating were developed and introduced as an important tool in finance and banking area. Credit models are become an effective way to evaluate the credit risk of clients. Many applications of the statistical techniques with more precisions and powers in predicting credit risk create benefits for financial institutions by helping them establish an appropriated strategy for risk mitigation.
This thesis presents the approach and results of an attempt at using logistic regression to develop a probability of default (PD) predicting model, a linear regression which is also supported by literatures of relevant factors by an ologit model for predicting the future loan group of any applicant. Both logistic and linear regression are applied to find out the fit models for commercial banks.
By choosing suitable models and deeply data analysis from Vietnamese commercial banks, the paper address almost big concerns in credit risk management and client credit worthiness assessment: determine suitable models for Vietnamese SMEs market for both predicting probability of default (PD), number of late payment days (ELG); specify factors that could cause a loan’s potential downgrade (PDL), important information that contributes in creditworthiness of an individual SMEs, the role of cut-off points in implementing banks’ risk appetite and suitable data treatment approaches.