Empowering consumers: The impact of underwriting innovation on credit accessibility in India

By Sashank Rishyasringa

In India’s dynamic digital lending landscape, fintech companies are navigating challenges to find growth opportunities amid rapidly evolving market conditions. Traditional credit assessment methods often fail to meet the needs of underserved populations, leading to limited access to banking services or exclusion altogether.

Over 60% of middle-income households are credit underserved. There are nearly 100 million credit cards in circulation, a number that pales in comparison to the 1.44 billion Indians residing in the country. However, given the expansion of India’s digital ecosystem, the emergence of data-driven credit underwriting techniques has revolutionized the industry, facilitating the efficient delivery of credit at the point of need and to those who have previously been excluded by the formal financial system. These underwriting techniques will play a crucial role in the consumer digital lending market in India, which is estimated to grow by 3x over the next 6 years.

Although digital lending in India is entering the growth stage, it is expanding rapidly and at a pace much faster than traditional brick-and-mortar lending. Total digital loan disbursements grew a whopping 12X between 2017 and 2020 and are poised to exceed Rs 47.4 lakh crore by 2026.

This phenomenal growth is driven by the emergence of Fintech lenders, which has enriched the diversity of India’s lending landscape. They possess a key advantage in catering to the unique needs of young borrowers who have limited to no credit history. For such lenders, data has become a cornerstone of the customer selection process. Through innovative data sources, machine learning, and advanced analytics, these lenders can assess creditworthiness beyond traditional methods like credit scores. This flexibility enables adaptation to evolving market conditions and borrower behaviors, ensuring effective risk management and efficient loan distribution. This also allows customer experience to take centerstage, even when extending services to those who are underserved.

Some FinTech lenders have witnessed a positive trend: their borrowers’ credit scores tend to improve, validating the effectiveness of their meticulous screening methods. This trend underscores the efficacy of data-driven lending models in identifying low-risk borrowers and reducing lending risks. Data-driven decision-making helps eliminate human bias and uses more relevant information about the borrower. For instance, the traditional credit score of an applicant is impacted by a repayment default 3 years back, while the borrower’s financial standing may be improved significantly during this time.

For larger loan amounts, typically exceeding Rs2-3 lakhs, Fintech companies have streamlined processes for faster and frictionless access to credit. Small-ticket loans (below Rs 50,000) comprise merely 0.3% of the total retail loan book size in India. Fintech lenders can disburse smaller amounts of credit at checkout within seconds and slightly larger amounts within hours, by rapidly assessing borrower risk and creditworthiness using data-driven models that adhere to regulatory guidelines.

To manage non-performing assets and maximize collections, meticulous underwriting practices are essential. Identifying both the intent and capacity of borrowers is crucial. This involves thorough due diligence, risk assessment, and loan origination procedures. The continuous monitoring of loans and borrower profiles has fostered the detection of early warning signs, to identify potential issues early on and implement remedial measures on time. Stringent controls and monitoring systems enable proactive addressing of financial distress and mitigation of default risks.

Fintech lenders strive to balance risk mitigation with providing quick, reliable loans, particularly for substantial amounts. They need to ensure that their data-driven models collect relevant data, from the right sources, and apply appropriate analytics or machine learning to draw accurate inferences. Elimination of systematic biases within the data is as important as identifying patterns. Data-driven credit decisions have enhanced responsible lending, integrating new data sources, richer data points, smart analysis, and flexible methods to broaden credit availability while prioritizing risk management. This strategy is poised to fuel Fintech lending growth, expanding access to funding while reducing lender risk and maintaining lending environment stability.

Overall, Fintech lenders must focus on ‘Selection’, followed by ‘Collection’. Selection involves picking the right customers upfront. These customers aren’t defined by whether they have a thick file or strong credit scores, but by whether they possess the intent and ability to pay. This involves consciously choosing to grow in a risk-first, sustainable model, even if it does not immediately translate into hyper-growth.

Once quality has been assured in the selection process, the management and control of collections significantly improve. Positive selection also addresses the growing issue of fraud within the industry and creates an environment wherein lenders are opting for sustainable growth and borrowers aren’t being overleveraged. This sets the precedent for consumer credit to expand within the guardrails set by the regulator.

The author is Co-founder and MD, axio.

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