Friday, July 19, 2024

Digital lending distribution landscape – Tier 2 and Tier 3 cities in India

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The financial landscape of India’s tier 2 and tier 3 cities is experiencing a profound shift, fuelled by the rapid digitalisation permeating the BFSI sector. Although traditional lending practices persist in certain areas, the advent of digital solutions is fundamentally altering the borrowing landscape nationwide. This digital revolution is not only reshaping how borrowers access funds but also revolutionising lending practices, making them more convenient, efficient and accessible than ever before.
The considerable adoption among underserved customer segments is further bolstered by the clarity and regulatory support outlined in the Fintech Lending Trends report by FACE (Fintech Association for Consumer Empowerment).
Interestingly, approximately 11 million credit cards were issued in the last fiscal year alone, highlighting a significant shift towards credit-based spending, even in semi-urban areas. Moreover, the lending landscape is witnessing a seismic shift towards digital disbursement channels.
According to a report by the Reserve Bank of India (RBI), digital loans accounted for over 6% of all commercial bank loans from April to December, marking a substantial leap from less than 1.5%. Digital lending startups are also capitalising on this momentum, with an estimated 120 million credit-ready customers in India, notably formally employed individuals without credit cards. At the forefront of this evolution lies customisation, a pivotal catalyst empowering financial advisors to craft tailored credit solutions that align with individual credit profiles. Leveraging sophisticated machine learning algorithms, digital lending startups arm advisors with the insights necessary for precise and effective selling strategies, shattering the confines of conventional manual processes.
Integrating credit bureau data serves as a cornerstone in this quest for personalisation, furnishing advisors with a nuanced understanding of a customer’s creditworthiness to recommend appropriate products. Moreover, the arsenal of comprehensive training resources offered by emerging startups plays a vital role in equipping advisors with the acumen required to navigate the labyrinth of diverse lending policies with finesse.
Artificial intelligence and machine learning are pivotal in revolutionising the lending sector, leveraging vast datasets to enhance approval rates, minimise risk and expedite funding processes. This technological prowess, coupled with evolving consumer aspirations, underscores the pivotal role of digital lending platforms in bridging the gap between aspiration and affordability.
Furthermore, Tier 2 and Tier 3 cities have witnessed a significant surge in smartphone availability and internet usage, which has facilitated access to digital lending platforms, making credit services more accessible to the underserved populace.
Sustained support from the Indian Government has bolstered the digital lending landscape further in Tier 2 and 3 cities. Initiatives like Digital India and Jan Dhan Yojna have been instrumental in promoting financial and digital literacy in the country. Fintech firms are also pivotal in democratising credit access in Tier 2 and Tier 3 cities by introducing innovative credit-rating models. While traditional models rely heavily on credit history, fintech organisations are leveraging new data sources such as bill payments, mobile phone usage patterns and social media patterns to assess an individual’s creditworthiness. In turn, this allows them to serve a larger customer segment, which was previously excluded from formal financial services.
Undoubtedly, the digital lending market presents a myriad of opportunities amidst its burgeoning demand. However, to capitalise on this potential, a customer-centric approach is paramount. As the sector continues to evolve, embracing innovation and prioritising customer-centricity will be imperative for sustained growth and success.

(The author, Aditya Gupta, is the CEO of Credilio.)

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