During a trip to the interiors of Madhya Pradesh, we met Shaktiman, a marginal farmer who owns less than a hectare of land and grows wheat and soya. He struggled for years to get formal working capital loans for his input purchases and was saddled with paying high interest to local moneylenders. Despite his persistent efforts to get formal credit, he has been denied bank loans over the years, as he has little collateral and bank officials have made decisions based on their subjective judgment rather than hard data.
Shaktimaan’s story is neither new nor isolated; 85% of farmers in India are small and marginal. Less than 40% of these farmers have access to institutional credit channels, and less than 30% have Kisan Credit Cards (KCC). KCC enables farmers to access timely and cost-effective short-term credit for crop production and related expenses. Even among the small subset of ‘banked’ farmers, issues such as loan processing times and inadequate ticket sizes persist.
It is not that banks and non-banking financial companies do not want to serve this segment. They too face challenges across the lending value chain. For example, regulations requiring limited or poor credit history and physical documentation, especially for new-to-credit and tenant farmers, hinder effective underwriting and consequently impact their ability to lend.
However, not everything is gloomy in the fields. The boom in digital data and agtech ecosystems, coupled with systematic efforts such as land record digitisation, promises a new dawn for India’s small farmers. Among the many emerging solutions across the credit value chain, leveraging alternative data to enhance credit risk assessment holds significant potential.
Alternative data includes a diverse range of non-traditional data sources that have become increasingly accessible due to the widespread democratization of data. With many small farmers often overlooked by traditional business rule engines (BREs), alternative data offers hope for broadening financial inclusion. These datasets provide information about farmers’ ability to pay and may include:
Environmental and remote sensing data such as satellite imagery and weather data.
Market and transactional data such as input purchases and production offtake.
Financial behavior and payment history data such as telecom data, utility payments, e-commerce transactions and insurance data.
Social data such as social media interactions, household behaviour surveys.
The use of satellite imagery is a major source that is gaining attention. Advanced machine learning algorithms are leveraged to analyze this data and provide insights on crop growth and health, yield estimates, soil moisture levels, weather anomalies, crop readiness, which directly contribute to lending decisions. This is aimed at reducing unpredictability and information asymmetry in agricultural lending and aiding the underwriting process. AgTechs are pioneering this solution and are partnering with lenders to embed these insights into their traditional underwriting models.
Such solutions can significantly enhance the agri-credit ecosystem by adding value on various fronts:
· Customising credit risk assessment of farmers with no/limited credit history.
Enhancing lead generation capabilities by identifying agricultural areas with high productivity potential.
Designing financial products that suit the seasonal cash flows and risk profile of farmers.
The recent announcement by a state government asking banks to remove the CIBIL requirement for crop loans has further increased the utility of such alternative datasets for banks.
However, some challenges remain in scaling up the use of alternative data. Our recent discussions with leading banks highlighted the following:
Reluctance of small and marginal farmers to leverage satellite data due to small amount of loan.
Concerns about the granularity and reliability of satellite data, as its accuracy is limited to the village level rather than individual farm level.
Integrating the solution with banks’ systems will require considerable time and effort.
· There is a growing need for tools that can effectively analyze data and gain insights from diverse alternative data sources.
Further, our conversations with agtech founders highlighted the limited ability to provide accurate information due to the limited availability of data such as standardised land records across states, crop cutting experiment (CCE) datasets, and the prohibitive cost of high-resolution satellite imagery.
Let’s imagine a scenario—what if there was a middleware platform that facilitated end-to-end digitization of the loan origination and underwriting journey, especially for new loan-seeking farmers. The front-end could work in a simple manner. All relevant farmer data required for loan origination, such as land records and Aadhaar, could be easily digitally retrieved through application programming interfaces (APIs). On the back-end, banks could also fetch multiple alternative datasets through APIs, perform credit assessment and approve loans within minutes.
The good news: Such a Digital Public Infrastructure (DPI) already exists, developed by the Reserve Bank Innovation Hub (RBIH), and initial pilots have yielded positive results. In our field visit, we saw how Shaktimaan approved KCC loans in less than 10 minutes. These platforms are constantly enriching themselves through various datasets, including payment data and warehousing data.
The foundation has been laid, and the proofs of concepts (PoCs) have been promising; now it’s time to scale it up. The government can create a framework for standardizing critical data such as digital land records, while ensuring access to crop-cutting experiment (CCE) data and subsidizing costs for high-resolution satellite imagery. State governments can further their progress on digitizing land records and cadastral maps. Development finance institutions can offer first loss default guarantee (FLDG) for loans disbursed to small farmers that are backed by alternative data. Lenders can adopt a saturation approach for PoCs in select states to disburse agri-loans. Successful pilots can serve as a blueprint for wider adoption. The startup ecosystem can develop an alternative data analyzer or digester that can synthesize multiple alternative databases and facilitate automated insights for banks’ BREs.
If we can put these infrastructures in place, the troubles of farmers like Shaktiman can become history. We are on the verge of rewriting the future of the Indian farmer by democratising access to institutional credit, and we must seize this opportunity.
This article is written by Sushma Vasudevan and Aparna Bijapurkar, Managing Directors and Partners, BCG India, and Varad Pandey, Partner and Director, Sustainable Finance & Investment.
