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Digital footprints: The new route to credit scoring?

Can payment banks fulfil the dream of financial inclusion in a developing country like India? The status quo answer to the question is ‘no’. So far, payment banks have not left any remarkable footprint in India, contrary to their international counterparts in other emerging/developing economies. The underlying reason behind this is that the payment banks in India are limited to just offering mobile wallet services. Unfortunately, that people can open a savings bank account with payment banks without having formal access to a credit facility proves to be a major hindrance in its growth path.

According to the GSMA report for FY16, India has 616 million unique mobile service subscribers, with another 330 million unique subscribers are expected to be added by 2020, which opens up bigger avenues for mobile technology to penetrate deeper in providing low-cost digital financial services in wake of the macro-level financial inclusion goal. In light of this fact, let’s evaluate.

Traditional methods

To achieve financial inclusion successfully, extending credit facility, especially to the lower strata of the economy, remains crucial. The question here remains: How to do credit scoring for people at the bottom of the pyramid?

Conventionally, banks and financial institutions would be vigilant while checking creditworthiness, mainly by using information from credit bureaus and associated databases. In the most contemporary way, banks would verify ‘customer identity’ and ‘the ability and willingness’ to pay. The ‘ability’ is usually certified by the income history of the applicant and outstanding debt, whereas ‘willingness to pay’ is checked with the help of historical credit performance.

Unfortunately, these conventional ways lack any solution to the problem of assessing the creditworthiness of the people from marginalised/weaker strata of the income groups. Due to irregular income patterns, cash transactions supersede other modes of payments. While it becomes extremely arduous to assess their ‘willingness to pay’, the situation is further aggravated by lack of any authenticated financial records to deemworthy the ability of a person to borrow from the organised credit facility, thus lending these groups into the vicious trap of a borrowing spiral from a set of unorganised lenders.

Mobile data

According to the RBI, about two-fifths of the rural households are still dependent on informal credit (Working Paper Series, 2013). It becomes quite relevant to understand what the missing planks could be in order to complete the road.

With the advent of technological advancement, mobile data information and digital footprints are becoming the preferred approach of checking the creditworthiness of an individual by fintech companies, to conduct predictive modelling on the available data. Every time a user uses his/her phone, he/she leaves behind digital footprints. Mobile phones provide a rich source of valuable information. The usage information of mobile phone could offer a virtuous indication of their lifestyle. For example, how text messages are structured (usage of grammar, words frequently used), call records (indicative data of social circle), location movement, mobile recharges, bill payments, other spending activities, frequency of changing mobile handsets, etc, could be utilised as data points in credit scoring.

Based on credit scoring, lenders could offer short-term customary credit products, further customised to meet users’ requirements. To make it more authentic, a loan could be directly deposited into the savings bank account of the applicant and its repayment could be deducted in installments via an auto-debit facility. To make it work conveniently, customers shall be able to apply for a loan using mobile apps. Globally, companies like Cignifi and First Access are leveraging alternate data like mobile usage and recharge frequency to build risk scores. Therefore, it carries the potential of converting data from the digital footprint to a financial track record, using technology.

More importantly, not just individuals, transaction data from merchants could also be used to extend the micro-credit facility to small businesses, with their digital footprints used for credit scoring for fast disbursal of credit facility that could promote ease of doing business. According to the GSMA FY16 report, if payment banks roll out a credit facility, revenue from credit services will grow from ₹60 lakh to about ₹277 crore in 10 years, which could generate adequate revenues for the payment banks.

Jain is Assistant Professor, Daulat Ram College, University of Delhi. Agnihotri is Assistant Professor, Lal Bahadur Shastri Institute of Management, Delhi

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