TerraCIS Technologies Ltd. (Formerly known as IL&FS Technologies Ltd.). | Better Ways to Assess Credit Risk
Better Ways to Assess Credit Risk
Better Ways to Assess Credit Risk
post-template-default,single,single-post,postid-23903,single-format-standard,,select-theme-ver-3.7,wpb-js-composer js-comp-ver-5.0.1,vc_responsive

Better Ways to Assess Credit Risk

One of an increasingly prominent tool used to facilitate the assessment of credit risk in mortgage lending is Credit Scoring Model based on credit history and other pertinent data related to the borrower. In simple terms, a Credit Scoring Model distinguishing between applicants who are likely to perform well on their loans from those that are less likely to do so. On the lender side, a Credit Scoring Model allows lenders to restrict credit to only the most credit-worthy borrowers and ensure wider availability of mortgages to borrowers at prices that better reflect underlying risks.

In order to facilitate an unbiased mortgage underwriting process, reduce costs, and promote consistency, ‘‘credit scoring’’ models have been developed that numerically weigh or ‘‘score’’ some or all of the factors considered in the underwriting process and provide an indication of the relative risk posed by each application. In principle, a well-constructed credit scoring system holds the promise of increasing the speed, accuracy, and consistency of the credit evaluation process while reducing costs. Thus, credit scoring can reduce risk by helping lenders weed out applicants posing excessive risk and can also increase the volume of loans by better identifying creditworthy applicants.

Generically, scoring is a process that uses recorded information about individuals and their loan requests to predict, in a quantifiable and consistent manner, their future performance regarding debt repayment. Scores represent the estimated relationship between information obtained from credit bureau reports or loan applications and the likelihood of poor loan performance, most often measured as delinquency or default. Scoring has been used to assess applications for motor vehicle loans, credit cards, and other types of consumer credit for decades, however, the adoption of credit scoring in mortgage industry is relatively new, because the mortgage industry is behavioural driven. However, technological advances in information processing and risk analysis combined with competitive pressures to process applications more quickly and efficiently are pushing the lending industry to incorporate scoring in the mortgage underwriting process.

Although credit scoring can reduce costs and bring more consistency to the underwriting process, its reliability depends upon the accuracy, completeness, and timeliness of the information used to generate the scores. For example, credit scores based on erroneous or seriously incomplete credit report information are not likely to accurately measure the risk posed by an individual applicant and may lead to unwarranted actions on an application.


Author: Ashish Soni, Senior Data Scientist, Terracis 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.

Post a Comment