IL&FS Technologies Limited | Credit Risk Modelling
Credit Risk Modelling – An Indian Context 
Credit Risk Modelling – An Indian Context 
23898
post-template-default,single,single-post,postid-23898,single-format-standard,,select-theme-ver-3.7,wpb-js-composer js-comp-ver-5.0.1,vc_responsive

Credit Risk Modelling – An Indian Context 

Credit risk is defined as the risk faced by lending institutions when their borrowers fail to pay their loan obligations as scheduled. Currently, screening of prospective borrowers is accomplished primarily through the underwriting process, whereby information needed to assess credit risk is collected, verified, and evaluated – basically applying data analytics to Big Data.

Lending institutions are careful while assessing the credit risk of the borrowers to whom they will be disbursing the loan. They establish guidelines for underwriters to follow when evaluating applications for credit. However, they still rely heavily on the experience and judgment of underwriters when assessing credit risk. Therefore the judgment of these underwriters collectively affects the incidences of delinquency and default, two important factors influencing profitability in lending business.

If we talk about the Indian Lending Industry, relying on subjective analysis of the underwriters has some important limitations. They differ in their experience and in their views regarding the relationships between risk and specific credit characteristics of applicants. In addition, the authenticity of the information collected, personal bias/interests of the approving authority, and lack of central data repository for verification are some of the other important limitations. Consequently, a lending institution cannot be sure that its underwriters are approving all applications that have risk profiles consistent with the objectives of the institution. In addition, because of the numerous and often complex factors mortgage underwriters need to consider, subjective underwriting which is time-consuming and costly.

Gaining a greater understanding of the factors that determine mortgage loan delinquency and default has become one of the objectives of mortgage lenders. A better understanding of these relationships holds the promise that lenders can more accurately gauge the credit risk posed by different applicants and increase the safety and profitability of mortgage lending.

In order to assess credit risk, lenders gather information on a range of factors, such as the current and past financial circumstances of the prospective borrower and the nature and also the value of the property serving as loan collateral. It is important to highlight that the precision with which credit risk can be evaluated affects not only the profitability of loans that are originated but also the extent to which loan applications that would have been profitable, are rejected. For these reasons, lenders continually search for better ways to assess credit risk.

In order to survive and thrive in this economic climate, credit risk professionals need to consider innovative means of decreasing default rates and improving the accuracy with which credit is issued.

Read our blog: Better ways to asses credit risk

mm

Author: Ashish Soni, Senior Data Scientist, IL&FS Technologies Limited

Ashish Soni is a quantitatively oriented analytics professional, with experience in statistical analysis, data mining, machine learning, and statistical programming. He has executed projects of varying complexity within the Finance, Healthcare, Education, Sports, and Human Resource Industry. He leverages his knowledge in mathematics and applied statistics, along with visualization and a healthy sense of exploration, to extract value from business data. An alumnus of IIT Bombay (B.Tech, Chemical), and a Masters Degree holder in Economics, and Post Graduate Diploma in Applied Statistics. On a professional front, he has almost 10 years of work experience in data science domain.
Share:

Post a Comment