The digital lending market of India stood at $75 billion in 2018 and is estimated to reach $350 billion by 2023, Statista. Interestingly, in the 2021 report—How India Lends—CRIF High Mark states that Small Ticket Personal Loans (STPL), i.e. loans under ₹ 1 Lakh, contributed to 50% of all personal loans (by volume) as of Mar’21. 65.7% of these loans have been provided by NBFCs. Another interesting data point is that 22.8% of the sub-₹ 10k loans and 15% of the loans of ₹ 10-25K were provided to borrowers under 25 years of age.
This upswing can be partially attributed to the increasing adoption of technology, waning manual intervention in the lending process. Almost every bank and NBFC in India now offers:
- Self-service facility— customers only need to interact with lenders if needed.
- Omnichannel communication— borrowers can choose between communicating through portals, apps, text messages or even social media platforms.
- Instant KYC option— customers can complete the legal procedures remotely.
India currently has around 200 million active credit users and around 800 thousand NTC (New To Credit) users are being born every year. However, the rapid digitalization of the lending process coupled with the surge of NTC users has brought forth newer challenges, such as conducting online background checks and accurately estimating a client’s repayment capacity in minutes.
These challenges, along with the abundant unstructured data generated from both existing and new users, have led lenders to use advanced technologies—like data analytics, and AI/ML algorithms—to analyse the data and automate the lending process.
With advanced analytics, lenders can gain insights from primary data sources (such as loan applications, GST statements, bank statements, credit history, and recent transaction details) to gain information on potential leads. They can also leverage open banking practices (that allow banks to share customer data with third-party financial institutions using Open APIs under consent from the user) to further discern borrowers’ credit behaviour, financial conditions, and potential future cash flow. This technology ecosystem is helping lenders tackle common inefficiencies in the existing credit decision-making process
One of the prime examples of automated credit disbursal is the “Buy Now Pay Later” (BNPL) scheme of fintechs (like the zero-cost EMI facility offered by some e-commerce sites). By making the algorithms do the heavy lifting—from KYC to lending—vendors have been able to capture the majority of the millennials, who often have no credit scores, with near-zero interest rates and easy instalments in just a few years.
With the help of Open banking, Data analytics and advanced AI/ML algorithms - fintechs, like Lentra, are helping banks democratize credit. They are making the credit decision-making rocess more programmable. This is done by first capturing data via Open APIs from Credit Bureaus, Banks, ITRs, GST filings, Social Media and device usage stats. Advanced algorithms process this data to gain deeper insights into borrowers’ creditworthiness. Finally, the software helps lenders create a custom risk assessment framework to align this automated process with their internal lending policies.
These fintechs enable lenders to generate custom risk scores utilizing advanced AI/ML models, perform customised eligibility computations, and/or automatically score individuals based on pre-set scorecards. This new age digitised lending process helps in lowering fraud, reducing overall NPAs and handling credit risks better.
For India to become a $5 Trillion economy, the lending space has to play a major role. Enabling access to credit securely and legally is the fastest way to crank up economic activity and boost GDP output. As fintechs continue expanding the horizons of uniting technology and lending, average citizens stand to benefit the most from this exchange.
If consumers are provided with loans right when they need one without being burdened by exorbitant interest rates and collaterals, more users (NTC and existing customers alike) will start inclining towards applying for one. This implies a greater amount of money flowing into the society in general, which in turn moves the Indian economy closer to the $5 trillion benchmark— one borrower at a time.