Let’s say- you want a loan.
You’re asked to get your “Credit Report” from a credit rating bureau. So you apply for your credit report. And if you’re 1 of the 5 people whose credit report is erroneous, chances are:
Your credit score goes down by a few points (at least)
You’re denied the credit.
In case your credit score goes down, you’re approved the credit, but at a higher interest rate. Or, you’re denied of it altogether. Either way, you know you’re in a bit of a trouble.
So it’s safe to say that for a borrower, the terms ‘Credit Report’ and ‘Loan’ are synonyms. The minute you apply for a loan, you know the bank you’ve applied to, will get your credit report pulled from the bureau. The approval/ rejection of loan, thus depends on your credit score mentioned in the credit report therein.
When credit report is such an important document that decides the future of your cash flow, what exactly are the variables used by a bureau to credit-score you?
The bureau uses-
The bureau considers traditional data sources like income and credit-mix for determining the credit-score. However, they are insufficient and the system does have some major drawbacks which are-
Heavy dependence on banks’ data: The only source of data for the bureaus, are the banks. Therefore the bureaus rely heavily on the banks. Also, any errors in reporting data by banks, leads to an incorrect credit-score. And it is believed that 20% of the credit reports are erroneous. (Wow!)
Untimely reporting: In India, the banks are required to submit data to the bureaus every month (30 days, precisely). However, many times there are gaps when it comes to compliance of this norm, which is why we sometimes find missing data on credit-score cards of people.
Dependence on past data: Lack/insufficiency of past data is a major constraint when credit-scoring. Also, inaccurate and unupdated past data leads to a misleading judgement of an individual’s credit-score.
Scoring for the “Credit Invisibles”: Credit invisibles are first-time borrowers (approximately 500 million people in India). Individuals who have never taken a loan, or there is negligible credit history, banks refer to them as a “0” or “-1” case. Thus, due to lack/absence of traditional data, bureau fails to assign a credit-score to these individuals.
Considering the credit aspect only: Bureau data primarily considers the credit-mix and risk appetite of an individual. It usually tends to ignore the expenses incurred by the individual. Let’s say the individual spends 80% of his salary on consumption, and does not have the savings’ mindset. In this case, his credit score maybe good because it is based on his income. However, his capacity to repay is not good, and the expenses are ignored, thus misleading the judgement of a lender.
So how is one supposed to solve this problem? What can be done to get more accurate credit-scores? Are there any means to bridge this gap between data availability?
Watch this space for our next blog (scheduled for 19th February) where we talk about how this problem can be solved by banks and financial institutions.