Thoughts on the Indian Economy

Thinking about assorted economic issues in India.

Sunday, January 18, 2009

Cell phones as credit scores?

Can we use cell phone payment records to help fill a bank's informational gap when it assesses risk?

From the point of view of a bank, lack of information about a potential borrower translates into higher risk of default and creates various distortions in credit markets like high interest rates, collateral requirements or credit rationing (borrower wants to borrow more at a given interest rate, but cannot).

Although improving, the financial system in India features considerable barriers to banking services like loans for the poor. Informational and enforcement issues manifest themselves in high fees, minimum deposit requirements, loan ceilings and lengthy loan processing. Beck et al all measure loan affordability by looking at minimum balances required for loans and the fees for these loans.

India has a pretty high minimum loan amount around 25% of GDP per capita of the country and the fees are about 1.5% of that minimum.

As pointed out before, more information about a borrower could reduce credit market distortions. The concept of a "credit score" or "credit report" seen in countries with more developed financial systems is still being implemented. Older institutions that filled in the gap like caste networks are fading due to increased migration and changing social norms.

How about using alternative information, that is information beyond a person's credit history. Specifically, how about using cellphone or utility payment records which would show a consumer's willingness and ability to repay "credit-like" obligations. Almost 300 million people in India use cellphones and if cellphone payment records could supplement credit records, the information base would be much higher. This argument is probably less potent if many of those are "prepaid" vs "postpaid" accounts, but it is still more information, right?

In a research paper called "Give Credit Where Credit is Due", the authors use 8 million credit files in the US containing alternative/nontraditional utility and telecommunication payment information and apply models that lenders use to make various credit decisions. They found including energy utility data increases acceptance rate by 9%, given a 3% target default rate. Minorities and the poor benefit more than expected from nontraditional data: Hispanics saw 22% increase in acceptance rates, 21% for Blacks, 14% for those 25 and younger, 21% for those earning $20K or less. They conclude that this increased data and information decreases credit risk, increases access, improves credit scoring models and reduces bad loans.


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