Availability of institutional credit for farmers at reasonable rates, is probably the most vital element of making the Indian farmer profitable and resilient.
More than half our farmers – an estimated 60-100 Million – cannot get institutional credit easily, because of incorrect or missing land records and are hence unable to provide land as collateral that the lenders seek. Some farmers can provide alternate collateral such as gold or jewellery, but most are unable to, and hence have to resort to informal lending sources including the middle men, to raise the 50,000 – 90,000 rupees on average needed for seed, fertiliser, etc. Even if lenders were willing to lend without collateral, there is limited information for credit-risk assessment. They sometimes rely on a local revenue official’s confirmation on farm revenues, but this documents’ quality is suspect due to corruption and incorrect data.
Clearly there is a need to think differently and this concept note describes an attempt to use data flows from disparate external sources, using an Agri Data Exchange, AI and “confidential computing frameworks” – real time and at scale, to relook at solving this problem in a very transformative manner for India’s 120 Million farmers. This will need extensive testing, policy tweaks and willing participation from the financial community. But we believe that the time is ripe for providing transformative impact to the Indian farmer – similar to how Aadhaar with UPI transformed digital payments. We cannot afford to delay this any further for the benefit of the Indian small and marginal farmer and for the development of Indian agriculture.
Year 2024. Our Vision – Credit assessment for the Small & Marginal Farmer.
The below scenario describes how farm credit could be assessed in the near future
Selvam, a rice farmer with a two-acre farm near Madurai, logs into the IAP using retina scan, fills in a loan request in Tamil, attaches photos of himself and the farm. He accords consent for his data to be accessed from different entities (Govt, Start-ups, FPOs etc): Aadhaar, geo-location, three year’s crop type, yield and earnings.
This data flows in, completing Selvam’s application and providing visibility of his farming history to all potential lenders. To evaluate credit risk, they use the combination of geo-location along with Aadhar to extract Selvam’s farm credit history and details of all existing and completed loans. The automated process allows a majority of the lenders to approve/reject the loan online within minutes. Artificial Intelligence flags issues needing a clarifying phone call.
Selvam chooses a financial institution who pays the seed supplier directly, crediting the balance loan into Selvam’s regular bank crediting the balance loan into Selvam’s regular bank using the UPI interface and creating an auto-debit for the month after harvest. The entire process is digital, with no paperwork. The bank also remits a small fee to the start-up(s) for providing Selvam’s cropping history.
Back in 2020, Selvam recalls filling several loan applications individually, spending money travelling to Madurai and loan sanctions took an average of two months. The IAP has reversed and democratised the process, giving him the power while lenders now bid for Selvam’s loan.
Comparing different offers transparently allows Selvam to choose wisely.
We can facilitate this, as in Selvam’s example, by processing large multi-year, Agri Data-sets, triangulating data real-time, from multiple sources (government, enterprises, start-ups):
- Drones / UAV (e.g. real-time monitoring of farms, surveying, 3D modelling)
- Satellites and remote sensing (providing past years revenue and yield estimation,
early pest warning etc.) - Farm sensors and management (e.g. for soil moisture, water and nutrient injection)
- Mobile cameras (e.g. soil quality and moisture levels and pest, nutrient deficiencies)
- Geolocation coordinates
- Aadhaar, etc.
Data from several entities is processed seamlessly into precisely actionable insight. This uses a “confidential computing” framework, masking raw data visibility across sources from one another – as well as from the final recipient of the credit assessment, i.e. the financial lender.
The signed loan papers along with the assessment documentation are stored in the Govt’s digital locker for auditability. The lender pays an assessment fee to the farm data providers, aiding their revenue stream. Using tools like video, voice, vernacular translation help facilitate farmer engagement.
The Agri Collaboratory 