Use of Technology in Alternate Lending

By Shailendra Sason, Product Manager, Indifi Technologies

Shailendra Sason, Product Manager, Indifi Technologies

Small and Medium Sized Enterprises (SMEs) are one of the key drivers of the economy, playing a crucial role in job creation in rural as well as urban India. Contributing to about 8 percent of the GDP, the SMEs have a strong potential to increase its impact significantly. However, despite the visible growth opportunities, the sector is riddled with severe challenges ranging for non-availability of affordable credit to lack of financial knowledge among the SMEs. The major factors which limit the traditional banking organizations to lend to SMEs are limited understanding of the SME business and lack of required financial information needed by banks to underwrite using traditional models.

Various startups coming in the Financial Technology sector are now working towards bridging the funding gap being faced by the SMEs. Start-ups such as Indifi, Capital Float, KredX are working towards building new age lending platforms that would utilize alternate data to develop a better understanding of the businesses thereby building more accurate underwriting models.  These models are based on complex Machine Learning and Artificial Intelligence algorithms and are rapidly moving towards replacing the manual underwriting with system-led data-driven decisioning platform.

The rise of Social data and the increase penetration of mobile and internet in emerging markets have opened a number of avenues to build a profile of the borrower in case traditional data points such as credit bureau history is not available. Sources can range from bank accounts, mobile usage data to borrower’s social footprint derived from Facebook, Twitter, LinkedIn and others. All these points feed into the models builds a holistic picture of the borrower that traditional lending organizations have not been able to achieve. As a result, these startups are able to provide better rates to borrowers as well as reduce probability of default in loans.

This data driven approach helps these financial organizations as well as the customers in a number of ways. Firstly, it radically improves the Customer Experience. The entire process of submitting an application to final disbursement can now be very effectively reduced to less than 48 hours. Using mobile and web based interface, the customer can apply for a loan, upload supporting documents, raise disbursal requests as well as repay the loan. This gives the customers a smooth experience, which is a significant improvement over conventional process.

Secondly, this approach also plays a critical role in reducing the operational expenditure of these loans. Automation of major steps during underwriting, disbursement and repayments not only reduces friction within the organization but also saves significant time and money. With the lending business being run on thin margins, operational efficiency would play a key role in making a start-up a leader in the field.        

In sum, we can say that the rise of alternate data sources in the form of social and other mainstream data will play a crucial role in finding the creditworthiness of an SME and these startups are leading the way. Big banks are also now realizing the true potential of sector and impact it will have on the SME sector. The rules of the games are currently being rewritten and the fintech startups are working towards enabling broader access to SME credit!

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