How did I get to this story?
Data and its visualization had always been of interest to me and given a choice between reading pages of analysis and data summary my eyes would always rover to discover the latter first. Because of the strong interest towards it, I picked up writing Python snippets that automated reading any kind of dataset from the web and display a visual representation of it. Having felt satisfied with the outcome I wanted to explore datasets from Domains that I had worked in viz Banking and Retail. While on a subconscious intent to search for Credit rating datasets my mind seem to have hooked upon a newspaper article on a leading Indian NBFC start-up having received another round of funding this Nov 2019 and I felt why not find how differently they assess Credit worthiness of their Customers before hunting for public datasets
What is the traditional criteria of receiving a loan?
Traditionally individuals or businesses receive loan from Institutional lenders (Banks, NBFCs) based on a sound Credit score residing with Credit Bureaus (CIBIL,CRIF High Mark, Experian, Equifax). The score is arrived based on the entity’s loan repayment history. However barely one-fifth of the Indian population has a valid credit score thereby constraining their access to credit and creating the vicious cycle – no score hence no credit and no credit hence no score3.
The additional factors which discourage this segment of credit-less borrowers from receiving a loan are the high Operational costs incurred for disbursing small sized loans by Institutional lenders. These Lenders also insist on Collateral to enforce willingness to pay by borrowers. High regulatory obligation by these Lenders mandate submission of elaborate documentation coupling it with increased loan processing time 2.
What is the Market overview?
The population size of these underserved entities is very huge launching the government to push forth a slew of policy changes and initiatives to drive up financial inclusion. The Retail front has close to 400mn customers in the banking sector with no credit history. Of them around 300mn customers have one account and the rest 100mn have no account1.
On the Business front MSMEs remain largely underserved in comparison to large sectors. There are 55.8mn MSMEs with 15% (8.2mn) being registered and remainder 85% (47.6mn) unregistered. They together generate more than 124 mn jobs and contribute to 31% of nation’s GDP and 45% of country’s overall exports. Their source for credit requirement estimated at INR 87.7tn comes from Debt (INR 69.3tn) and Equity (INR 18.4tn). The below figure summarizes their overall Credit supply sources 2.
Figure 1: Total debt supply to MSMEs
What challenges are being addressed to get the outcome?
This cohort of Borrowers seek Lenders to offer differentiated services in their loan products: Unsecured loans with Low regulatory obligations, Minimal operational requirements, comfort with higher risk profile and alternate Credit Assessment methods 2.
Figure 2: Fintech Lenders service offering
How have these challenges been overcome?
An expanding Internet penetration (636.73 mn users, 47% as of March 2019) and increase in the number of smartphone users since 2010 (468 million as of 2017), has paved way to the growth of many digital Businesses and subsequent adoption by Consumers to a digital way of life. This technology disruption pressed the rise of many Fintech Lender firms who created Alternate lending models and drove majority of their processes online with automation.
Figure 3: Alternate lending models of Fintech lenders
These Alternate lending models operate on alternate data from external sources along with traditional data from Credit Bureaus, for Credit underwriting and approval. The merit in alternative data is access to behavioural patterns of individuals who may not have a loan account or collateral, but have a phone connection. Any behavioural pattern is predictive and can be analysed for decision making 1.
Figure 4: Data source for alternate and traditional lending models
References
- https://economictimes.indiatimes.com/markets/stocks/news/alternative-data-can-gauge-creditworthiness-if-law-permits/articleshow/68748552.cms?from=mdr
- https://www.intellecap.com/wp-content/uploads/2019/04/Financing-Indias-MSMEs-Estimation-of-Debt-Requireme-nt-of-MSMEs-in_India.pdf
- https://www.pwc.in/assets/pdfs/publications/2016/non-banking-finance-companies-the-changing-landscape.pdf
