How is contextual lending architecture important in fueling new India’s economic growth?
Synopsis: In today’s blog, we will discuss the transformative impact of the adoption of contextual lending architecture by financial institutions on streamlining lending processes, driving financial inclusion, and catalyzing India’s economic growth.
The credit ecosystem is undergoing a revolution, and the establishment of contextual lending architecture is at the forefront of this change. Lenders are no longer fixated on product-specific underwriting; instead, it has become more context-driven.
With alternate sources of datasets becoming readily available and consent-based data-sharing frameworks in place, lenders are deploying artificial intelligence (AI) and machine learning (ML) based models to undertake contextual lending that goes beyond mere credit score evaluation.
But what is contextual lending architecture, and how can it give a fillip to India’s growth story?
Contextual lending architecture: The key to creating personalized experiences
Offering digital lending solutions is not the key differentiator anymore; personalization is. Indeed, as per an Experian study, 83% of Indian customers have high expectations from their digital experience, compared to an average of 55%.
With 52% of the Indian adult population actively using fintech services, the provision of tailored lending experiences has become a necessity—and contextual lending helps achieve this by focusing on the “what” and “why” of an application.
Contextual lending architecture is a lending framework that leverages APIs, predictive analytics, and machine learning algorithms to determine a borrower’s creditworthiness and offers tailored loan products after factoring in their context, i.e., their unique circumstances and requirements.
The context can vary on several fronts such as the purpose behind the loan, the magnitude of the loan relative to income, loan tenure, terms of credit, hierarchy in repayment, availability of security, etc.
Lenders focus on specific alternate datasets to better understand the prospective borrower’s context. To illustrate, to judge a borrower’s repayment capacity, the lender may check for the excess of monthly cash flows after providing for rent, utilities, food, and other expenses.
Consequently, by offering tailored loan products, contextual lending architecture expands access to credit to the unserved and under-served population, bridging the credit gap, fostering entrepreneurship, and enhancing financial inclusion.
Contextual lending architecture: Creating the ecosystem
In addition to revolutionizing digital payments, the continued innovation in the India Stack based on application programming interfaces (APIs) has also enhanced credit access by offering pre-sanctioned loans on UPI. However, India Stack’s even bigger achievement lies in making contextual lending possible.
Coupled with the implementation of the Account Aggregator (AA) framework—an entity that enables individuals to securely and privately share their personal financial data with lenders, including banks, NBFCs, and other financial entities—contextual lending has been democratizing access to credit.
This is because borrowers can now share their aggregated data from multiple sources, such as GST feeds, banking data, tax information, securities data, etc., on a continual basis, with the option to revoke consent at any time. Lenders process this information to gain valuable insights into a borrower’s profile and offer contextualized loan offerings.
To illustrate, GST data provides insights into an MSME’s monthly sales, revenue distribution, turnover, and gross profits, which combined with bank statements, deliver a holistic picture of the company’s cashflows. These insights get factored into credit evaluation processes, making it easier for MSMEs to avail of business loans even without a thick credit file.
Contextual lending architecture: Fueling India’s growth
As credit demand continues to remain unmet—SMEs alone account for an Rs. 20-25 trillion credit gap—on account of low credit scores, poor documentation, a lack of collateral, and other reasons unique to the borrower’s circumstances, contextual lending holds the potential to truly transform the credit landscape, making it “customer-centric”.
Historically, banks have kept their customers captive, as they had exclusive access to key proprietary data by virtue of holding their accounts. Contextualized lending in conjunction with the AA framework has leveled this lending space by providing financial institutions with alternate data without compromising on security, privacy, or safety.
The availability of alternate data has improved credit underwriting, enabling fintech lenders to offer personalized loan options to their clients, augmenting their growth. In fact, lenders utilizing big tech and ML algorithms have been leading the charge in loan origination, particularly in new-to-credit (N2C) and sub-prime customer segments.
An Experian study states that over half of these N2C borrowers eventually raised their credit scores to over 700 based on their performance.
Fintechs have deepened credit access in the remotest corners of the country, reducing geographical disparities. They have provided over 32% of short-term loans to borrowers from Tier 4 cities demanding small-sized loans of under Rs. 10,000.
Moreover, armed with actionable insights from the available data, lenders have been tailoring their loan offerings to their customers’ requirements. The availability of GST data has encouraged cash-flow-based lending, limiting the need for collateral.
The contextual lending architecture also facilitates lenders in cross-selling product offerings in the form of tax planning, GST compliance, and consulting, which further aids businesses in improving their operations. This bundling of services begets more data, furthering network effects and increasing economies of scope and scale.
Thus, with the contextual lending architecture in place, lenders have been sourcing loans to underserved segments, especially MSMEs, which already account for over 30% of India’s GDP, and hold the potential for more. By bridging this colossal credit gap, India can well be on its way to becoming a $5 trillion economy by 2025.
How is Protium’s lending model transforming the industry?
Protium, with its engineering finance philosophy and risk-based, proprietary analytics-driven tech platform, has been redefining how lending is done.
- Contextual, time-bound consent for data sharing: The Protium model has leveraged the creation of a consent-driven architecture by automating and embedding the permission given by its potential borrowers to use their data for credit evaluation over a certain period. This has led to the maximization of data usage, without risking end-to-end data privacy and security, enabling borrowers to enjoy customized loan options, despite a dearth of repayment records.
- Personalized offerings: As our data analytics and ML-driven algorithms capture information at several touch points of a customer’s journey, they arm us with real-time insights to build a comprehensive view of the borrower and predict their behavior. Our loan offerings are accordingly tailored to the customer’s context, which can be adjusted as more data becomes available.
- Localization of Services: Protium has been localizing its services and content, both in terms of text and voice through its omni-channel presence across its digital platform and physical branches. Catering to customers in their native tongue enhances consumer protection and makes it easier to onboard them digitally, eventually reducing offline operating costs.
The bottom line
India’s growth story continues to be bogged down by its credit gap, which restricts investment and infrastructure development. By giving impetus to contextual lending architecture, entrepreneurs and MSMEs can avail themselves of previously inaccessible loan options that are not only affordable but also personalized to their unique needs.
However, to fuel even higher economic growth, there is a need to introduce universal lending protocols that will standardize API integrations—an absolute necessity for stimulating platform lending—and further the adoption of consent-driven architecture.