Despite the large scale embrace ofdigital technology, a significant proportion of India’s population remains under banked and has little access to formal, institutional credit. Demonetization and the government’s digital India drive has propelled a huge spurt in digital banking with an explosion in the number of new bank accounts (Jan Dhan Accounts),e-wallets opened, and volume of electronic transactions. However, commensurate levels of credit access are yet to be achieved. Sample this statistic for instance -a staggering 83% of the Indian populationare active mobile phone users, 32% have access to the internet, but only 25% have access to formal credit sources.
What accounts for this disparity?
A major drawback of traditional models of credit scoring is their limited reach in developing economies like India, where low-income segments and first-time borrowers are largely excluded from the purview of formal credit providers. Due to the lack of access to institutional credit sources, the only available option for many of these borrowers is unorganised and unregulated credit providers– a fancy name for the local moneylender. Further, records of unorganised borrowings are not considered valid by banks and lending institutions thus further restricting the formal credit options of this large segment of borrowers.
Rapidly increasing digital activity is however, opening the credit door to millions of low income or first time borrowers in India by means of alternate credit scoring models that do not require prior participation in the credit system.Keep in mind that of the 75% of the population that is outside the formal credit system, a large number use mobile/smartphones, are active on social networking sites and other online platforms. This electronicengagementleaves a large digital footprint that can be utilized to create credit scoring models, focused precisely on those segments that have been hitherto shut out of formal credit channels.
Alternative credit scoring models use a range of unidimensional and multidimensional data available in the digital footprints left by users on social media networks like Facebook, Twitter, and LinkedIn and other data sources like telecom records, government reports, retail, and e-commerce websites.It is important to note that these data sources can be accessed only with the explicit approval of the borrower. The information available on these channels goes beyond the usual financial and credit history of EMIs and credit card payments. Instead, the rich dataavailable offers up alternative paradigms to determine an individual’s creditworthiness based on their digital activity that might not necessarily be credit-related. The real challenge lies in mining the available data effectively and making meaningful correlations that result in reliable credit scoring. The result is the opening up the credit space to millions ofcustomers who are currently shut out due to the lack of a formal credit presence.
While it is an exciting field, there are certain inherent challenges in creating these scoring models. One is the sheer volume of data involved in alternative credit scoring, which is higher than what is required in traditional scoring models. It also requires technological and analytical expertiseto accurately collect, classify and analyse the information and produce meaningful results. And last, it needs a robust regulatory environment that keeps pace with the fast-moving developments in this field.
Developing alternative scoring models is a huge opportunity especially in a large developing credit market like India. Surprisingly, there are fewsuch models available globally, even in developed fintech markets like US or Europe, and none have been universally validated.India is showing the way in terms oftechnology as well asin the growth of a lending environment that is receptive to such initiatives. Collaborations between financial institutions, data analytics firms, retailers and lendersare ushering in a new age of financial inclusion for consumers, with a tremendous potential for expanding the lending market on an exponential basis.