Credit Bureaus, Scoring & Technology

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Over the past few years, Retail lending has become the focus of the banks and the NBFCs on account of the contrasting fortunes of commercial and retail credit. In contrast to the trends of slowing growth and rising delinquencies trends exhibited by the commercial lending space, retail lending has exhibited significant robust growth accompanied by declining delinquencies. The purpose of this article is to illustrate the ways in which the success of retail lending has been driven by the change in  business model driven by the advent of Credit Bureaus like TransUnion CIBIL and the concomitant usage of technology. 

Credit Bureaus have facilitated the transfer of comprehensive consumer information to all consumer lenders by institutionalizing the process of information sharing. Further, they have taken the lead in building credit scores – statistical algorithms that enable the usage of comprehensive consumer information to rank-order consumers according to user-defined quality (delinquency, response to a marketing campaign, collection probability etc.) in a clear, transparent and objective manner. A prominent example is the CIBIL score that is considered to the industry standard in the consumer lending space.






The second phase of growth (FY11 Onwards) has been significantly better as the confluence of the structural trends of the advent of the Credit Bureaus like TransUnion CIBIL, increasing information technology intensity and the resultant development of credit scoring based automatic decision making has radically redefined the consumer lending business model from “manual, judgmental and relationship driven” to “digital, credit scoring and transaction driven”. As the following sections clearly enume rate this paradigm shift in the business model has translated into multi-faceted benefits for consumer lending industry, consumers and the aggregate economy. 

The CIBIL Score has been used by Consumer Lenders in the following two general ways: 

• Incorporating the bureau information and the bureau scores along with their other information to generate proprietary scores. 

•  Combination of proprietary scores and bureau scores in a rule-based quantitative decision matrix. 
















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