Banks and merchants have shared skewed dynamics since the advent of commercial trade in exchange for money; merchants require monetary support from lenders to operate their businesses but are often denied due to the nature of the trade they intend to carry out.
Fast forward to the 21st century, and the underlying principle remains the same- small merchants are considered high-risk and high-cost clients to acquire and retain. Due to a pre-existing credit gap and lack of conventional data required to authorize financial documents, the scales of supply and demand between the two parties have remained imbalanced.
D for Digital; D for Difference
The resolution of the problems faced by both sides lies in something that has become an integral aspect of our daily life- data. However, idle data is not that significant. It is granular, dynamic data that bridges the gap. Smartphone penetration has been on a steep rise in developing countries- the concerned demographic seeking financial inclusion. Banks already possess treasure troves of data but are unable to extrapolate them into innovative lending models. This is where digital SME lenders are making the difference by streamlining the flow of data in a bi-directional manner and implementing online interfaces to reduce the occurrence of conventional bottlenecks such as hefty paperwork and long approval pending periods.
Leveraging large sources, digital SME lenders compile their data from real-time sales, tax filings, business accounting and bank account money flows. Affiliate marketing, loyalty programs and gift card offers are also designed to attract merchants and gain access to fundamental data about them transparently. By providing value-added services and e-commerce solutions, the accessibility merchants have to formal credit is improved drastically.
The winning feature of digitally evolving lending models lies in the disapproval of a “one size fits all” framework. Teams of dedicated data scientists and underwriters work on developing custom scoring models based on inputs of geographical, cultural and local credit bureau information to rank merchants on various parameters- such as creditworthiness and delinquency rates. Psychometric tests, customer reviews, industry margins and current vs future product usage forecasts are taken into consideration while designing scoring models, such as India’s Capital Float.
Is India’s MSME and SME sector sufficiently equipped to be ‘Atmanirbhar’?
India has made commendable strides in unifying population data on digital omni channels- Aadhar (biometric identity), e-KYC (consensual identity information sharing), DigiLocker (government authorized cloud portal for storage of documents), e-Sign (digital document signing) and UPI (unified payments architecture enabling universal transactions). With a vision to utilize digital data as a surrogate for otherwise lacking credible information, Indian SME lenders rely on credit reporting service providers, bank records, CRM systems, tax records and government identity records to form their scoring models.
Due to regulatory impositions by the government, digital SME lenders in India cannot directly lend to merchants, and this is where the partnership with banks arises. Seemingly a logical step towards financial inclusivity and sustainability, the example of the partnership between Capital Float and IDFC Bank can be taken into consideration. The ethos of this positively symbiotic relationship lies in IDFC Bank gaining access to Capital Float’s digital pool of customers and Capital Float can in turn utilize IDFC Bank’s balance sheets and product innovation to cater to this customer segment.
Concluding, it is clear that merchant acquisition cannot be achieved by the general methodologies applied over the years. Innovation is here, and it is set to be driven by alternative data.