Satellite data can bridge agricultural data gap, strengthen credit underwriting

By delivering real-time insights on crop yields, weather patterns and pest infestations, satellite data and imagery enable financial institutions to make more precise risk assessments.

Conventional underwriting practices have proven ineffective in assessing the creditworthiness of agricultural loans, primarily due to the absence of historical credit data and the inherently unpredictable nature of agriculture. Leveraging satellite data and imagery, however, offers a transformative solution. By delivering real-time insights on crop yields, weather patterns and pest infestations, satellite data and imagery enable financial institutions to make more precise risk assessments. This can help expand access to institutional credit for small and marginal farmers, liberating them from the clutches of exploitative money lenders.

In the ever-evolving landscape of agricultural finance, two critical players — banks and farmers — are at the centre stage. The former holds the key to providing the necessary capital through loans. The latter, the stewards of the land, use those loans to invest in farming operations that sustain food supplies. At the heart of this complex and symbiotic relationship lies the underwriting process, a financial umbilical cord that connects these two parties. For banks, it is a meticulous process that involves evaluating the creditworthiness of farmers, determining the risk associated with lending to them and setting the terms of loans. For farmers, it is the gateway to securing the much-needed capital for planting, nurturing and harvesting crops.

Lack Of Reliable Data 

However, traditional loan appraisal and underwriting practices often falter when applied to agricultural loans. Imagine a bank's underwriter tasked with assessing the creditworthiness of a small-scale farmer. As the underwriter delves into the intricate details of the farmer's financial history and land use, he soon encounters the first hurdle — a lack of reliable data on incomes and assets of the prospective borrower. The financial landscape of many small-scale farmers is often marked by informality and irregularity. In the absence of well-documented credit histories and income statements, banks face the daunting task of determining the farmer's ability and willingness to repay the loans.

As the underwriter delves deeper into the labyrinth of agricultural lending, more challenges emerge. Unlike urban sectors with easily accessible credit reports and financial statements, the rural setting presents a starkly different reality. The risks in agriculture can range from unpredictable weather patterns, droughts and floods to pest infestations, each capable of impacting the farmer's income. Traditional underwriting models are often ill-equipped to consider these ever-changing variables, leaving banks grappling with incomplete risk assessments.

According to the Agriculture Census 2015-16, there were 12.56 crore (out of a total of 14.60 crore) small and marginal farmers with land holdings of less than two hectares. Despite strict enforcement of priority sector lending (PSL) norms, roughly half of them had no access to institutional credit.

Most small and marginal farmers engage in subsistence farming with transactions that rarely find their way into formal records. As a result, institutional lenders tend to be apprehensive about extending loans to small farmers who don’t have verifiable credit history. That leads to the virtual denial of credit to such farmers who then approach high interest rate charging informal lenders and end up with debt piled up that they can’t get away with easily. Improved access to cheaper institutional credit would be vital for agricultural households, yet it often remains frustratingly out of reach today.

Solving The Data Problem 

Thus, in this intricately woven tale of agriculture and finance, it is evident that the missing link is robust, comprehensive and timely data about crop yields (and in turn, farmers’ income) which are strongly influenced by unpredictable monsoon rains and unforeseen changes in weather patterns. Lenders — banks as well as NBFCs — are hindered by the scarcity of such critical information needed to make lending decisions.

One of the key solutions at the forefront of this transformation is satellite technology – a game-changer that holds the potential to bridge the data gap and bring transparency and resilience to the agricultural lending landscape. Embracing it is not a choice but a necessity. The shift towards satellite data is imperative to foster broader financial inclusion and to mitigate the inherent uncertainties and unpredictabilities that characterise agriculture.

Remote sensing, weather and earth observation satellites can provide the much-needed images, data and insights about crop yields, spatial and temporal distribution of monsoon rains and pest infestations that banks and fintech companies can rely on to do their risk assessments.

Satellite data and analytics startups such as SatSure, CropIn, AgroStar and RML AgTech are able to provide detailed reports based on satellite data that encompass reliable information on land location, irrigation conditions, cropping histories and yield estimates. That can help banks and non-banking financial institutions in making informed decisions for loan underwriting. These reports can also be used for detecting fraud by comparing and matching data provided by farmers and then understanding their actual credit requirements. Thus, these startups are enabling financial institutions to extend loans to small and marginal farmers who were once underserved, and promoting financial inclusion.

Private sector lender, ICICI Bank is the first bank which started using satellite data to make credit underwriting decisions with respect to loans through Kisan Credit Cards. Many more banks are contemplating using services of satellite data and analytics for underwriting agriculture loans.

To summarise, in India's agrarian economy, where the credit demand-supply gap hampers productivity-enhancing investments, satellite data and analytics companies are crucial facilitators. Their role in bridging the data gap, fostering financial inclusion, and supporting the growth of the agricultural sector cannot be overstated. This transformative approach holds the promise of a win-win solution for lenders, farmers, and the broader Indian agricultural landscape, offering a brighter and more equitable future for all involved.

Prerna Sharma Singh is a director on the board of Indonomics Consulting Private Limited, a policy research and advisory startup, and heads its agriculture, food and retail practice. She tweets at @AgriFoodRetail. Views are personal, and do not represent the stand of this publication.