In an interview with TechGraph, Lalit Mehta, Co-Founder of Decimal Technologies said, “When it comes to SME digital lending, we have barely scratched the surface.”
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TechGraph: What is the state of SME digital lending in India? Why do some SMEs go for informal credit sources?
Lalit Mehta: According to estimates by the World Bank, the current credit gap in India for MSMEs is around $380 billion. There is an unmet demand for credit from SMEs. We, at Decimal Technologies also noticed and are attempting to address this gap through our AI-enabled digital lending marketplace, Saarathi, where MSMEs are one of the main borrowers.
SME digital lending still has a long way to go but fintech companies are rapidly addressing the lack of formal credit access for the sector. Since SMEs largely operate in semi-urban and rural areas, technology is a key enabler in expanding the reach of credit to these underbanked and unbanked corners of the country.
Technology is bringing advancements in SME lending by transforming the traditional underwriting processes to be more inclusive. With the integration of Artificial Intelligence and Machine Learning, assessing creditworthiness through alternative data, rather than relying solely on credit history or other documents that people living in semi-urban and rural areas do not always have.
The lending industry in rural India is largely informal. Inadequate credit and financial awareness, lack of proper documents, and limited lender options are some of the challenges faced by the borrowers. Rural populations have to rely on informal sources of credit due to low income and a lack of substantial resources to be kept as collateral in banks. Due to this, they take loans from money lenders who give them loans with or without collateral at very high-interest rates.
TechGraph: How will alternative data sources help bridge credit access among SMEs and small entrepreneurs?
Lalit Mehta: Lenders are now using alternative data sources such as social media activity, buying behavior on eCommerce platforms, telecom usage, etc. to assess the risk and credit behavior of borrowers. This is not possible to do through traditional scoring methods, where the lender has to depend on the documents submitted by the loan applicant for risk assessment.
Both structured and unstructured data are collected by the lender and data analytics further helps in deriving intelligent insights from this to process primary verification. By using analytical tools, lenders can do customer segmentation that helps in identifying the financial activities of prospective customers and serve them better with customized products. Data analysis can also help in understanding geographical segmentation through which lenders can bring the unserved customer category into their customer base.
Digital data-based alternative credit scores give a holistic view of a borrower’s creditworthiness and associated risks for credit underwriting to the lender. Deploying credit underwriting mechanisms powered by new-age technologies like artificial intelligence, machine learning, and data analytics, a huge volume of data from various disparate sources are often compiled and analyzed extensively with the assistance of algorithms to derive useful insights.
TechGraph: Can AI and ML solve the major problems of ‘loan application dropouts’ in the Indian lending ecosystem?
Lalit Mehta: The new generation of banking customers are looking for convenient and time-saving modes of loan request submissions. Even lenders (Banks & NBFCs) are now offering omnichannel platforms for loan disbursals to their customers and third-party agents. Right from the initial loan application, verification of documents to loan closures, lenders are extending digitally enabled services via their mobile applications or internet banking with the new-age customers.
Complex and time-consuming loan application processes are the key factors responsible for loan applicants dropping out. In this increasingly digital world, consumers expect a quick application experience that can provide them easy credit access. The use of AI and ML can fulfill this expectation and, consequently, reduce the number of dropouts for loan applications.
As loan assessment is a data-heavy process, using AI and ML technologies can streamline the data and extract the necessary information to determine someone’s creditworthiness in a short amount of time. These technologies will not only help in speeding up the decision-making process but also in reducing risks and boosting revenue by retaining applicants. Saarathi, being built on Decimal’s proprietary and trusted No-Code Low-Code platform, makes the approval process 5X faster for customers.
TechGraph: What does the next decade of digital lending for SMEs look like?
Lalit Mehta: When it comes to SME digital lending, we have barely scratched the surface. The outbreak of the pandemic acted as a catalyst and has accelerated the adoption of digital lending platforms among small businesses as traditional banking has remained a challenge for a large number of SMEs in India. The rising adoption of smartphones and high internet penetration coupled with digital lenders bridging the information gap has also pulled traditional and new-age borrowers from India’s hinterland towards digital lending platforms.
At Decimal, Saarathi is helping in bridging the credit gap in India by making the traditional credit channel efficient and transparent. Saarathi offers a completely digital and transparent process that sends data into the lender system that helps in reducing frauds and in making loan files trustworthy.
The digital lending ecosystem in India is currently evolving and is a step towards making credit more inclusive and accessible to the remotest parts of the country. With the help of our technology platform, we want to empower the last mile (Channels) and help SMEs get access to formal lending digitally.
