Agriculture credit risks can be defined in terms of farmer’s ability to repay loans. Most of the farmers in India do not have a bank account and tend to depend on non-institutional sources like local money lenders. These local money lenders thrive on high risks and charge high-interest rates. One of the important reform that took place in 1969 was bank nationalization to increase the availability of the credit to agriculture and free farmers from the holdings of the private money lenders. Banks were advised to allocate lending resources in the rural areas for the purpose of increasing the credits in the rural agriculture sector which led to an expansion of banking branch network in the rural areas. Even after expansion of the banking network in the rural areas most of the farmers are not able to obtain capital help from the banks or insurance companies.
The small and marginal farmers across the world are dependent on credit to perform farming every season. The source of credit for these small and marginal farmers is from formal credit lending institutions and informal credit lenders. The problem farmers face with formal credit lending is proving their credit worthiness. Though, formal credit lending institutions offer the benefit of nominal market interest rates, the cost of establishing the credit-worthiness is completely offloaded on the farmers. This leads to farmers borrowing money from informal sources of credit such as traders, relatives, landlords, and friends. They also borrow from the local money lenders due to the cultural and location proximity.
In India for example, formal credit lending institutions use personal data of the farmer along with financials and collateral to evaluate his or her credit worthiness. However, the regional and external factors which affect agriculture like agricultural data, econometric and weather data find limited space in defining the credit worthiness of a farmer in the current institutional lending process.
Agriculture credit risk rating which can be defined in terms of farmer’s ability to repay loans is therefore an inexact science and most of the farmers are locked out of access to capital. This situation is further exacerbated due to lack of data, institutional credit lenders face problems of inefficient credit policy, underwriting, calculating inherent risk for a borrower, and data asymmetry before lending and after lending. Considering the importance of credit in the agricultural growth of India, adoption of new technology and novel risk models for agricultural lending is the need of the hour.
SatSure’s solutions brings a different level of scale due to the primary data insights on the credit decision making driven through satellite and weather data analytics. The solution is designed with easy integration to the bank’s Loan Life Cycle Management Systems, and being cloud native makes the processing and delivery of large area data insights scalable with low turnaround time. The solution is designed from a bank’s point of view so that direct decision insights can be absorbed by the credit, risk, and operations teams of the banks, via web dashboards for management oversight and a mobile application that is integrated with the dashboard so that the decision automation is percolated as actions on the field for the operations team staff across the geography of its operations.