The efficacy of the age old brick and mortar concepts are being challenged by fast moving technologies which are lighter on time and cost. Globally, the banking space including the shadow banking segment, has also been ushering in effective FinTech driven ecosystems that have enhanced delivery quality in terms of customer delight and business volumes.
Analysis of creditworthiness of a prospective borrower is one, if not the most important, activity in the entire life cycle of loan underwriting. On an individual level, a lending entity like a bank or a Non-Banking Financial Company (NBFC) typically relies on traditional data like repayment history of the borrower, periodic income source and size - proof of such incomes being realised in the bank account, self declaration of other assets and liabilities, numbers from the borrower etc.
The Credit Information Companies (CICs) or credit bureaus are the repositories of credit information which they collect from banks, credit card providers, NBFC, HFCs and other financial institutions. Each loan details and the service history are captured in a virtual dossier identified by the Permanent Account Number (PAN) of the borrower. In India, such CICs are currently four in number - TransUnion Credit Information Bureau Limited, Equifax Credit Information Services, Experian Credit Information Company and CRIF High Mark Credit Information Service. While the traditional data is largely dependent upon the credit manager, an upcoming trend is of using ‘Alternative Data’ which is basically the digital footprint left by the customer and mined by the FinTech companies.
What is the spread of digital data?
Over the years, the consumers have been getting used to internet based online shopping wherein the commodities and services alike can be bought from sellers and service providers. Digital footprints go beyond social media and can be a transactional SMS on the customer’s mobile number when he hails a cab, orders a pizza, buys an insurance policy or books a movie ticket. It can also be the total amount spent on talk time and internet. Analytics algorithm can interpret these scattered data and throw light on the lifestyle preferences of the borrower-tobe in terms of assignable numbers.
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