AI in agriculture—powering smarter agri lending decisions

Team Cropin
October 29, 2020
6 min read

Estimates suggest that approximately 2.5 billion people live in 500 million rural households across the globe, and they are primarily engaged in smallholder farming. A vast majority of this population resides in developing countries where access to agricultural finance is limited due to the inherent unpredictability associated with the sector. It isn’t surprising then that even though a large proportion of the population in such economies is dependent on agriculture for their livelihood, financial sector institutions are not as forthcoming when it comes to lending to farmers. In the year 2014, the credit demand in the Philippines to produce essential commodities like rice, corn, sugarcane, and coconut was $11.3 billion, while the credit disbursed by banks amounted to only $3.4 billion. This is indicative of the huge gap that exists between the need and availability of credit in agriculture. Research indicates that the demand for food will increase by 70% in the next three decades, which will need at least $80 billion annual investments to meet this demand. It goes without saying that we need to find a solution to the problem of agricultural credit, and we need to find it fast. The food processing industry too stands at a crucial juncture when it comes to ensuring brand loyalty. In fact, customer loyalty has emerged as the top concern for the industry, with 52% of respondents rating it as a challenge, as per a survey commissioned by Cropin. Interestingly, the other top industry concerns – 44% of respondents said last-mile traceability was a challenge, followed by 42% voting for changing customer preferences – hold some of the answers to addressing the brand loyalty issue.

Smallholder farmers face specific challenges when it comes to accessing timely finance and credit facilities. Apart from the unpredictability associated with traditional agriculture, aspects such as poor or the lack of credit histories, inability to track growth and yield scientifically, and overall instability of income make it a risky sector for investment. However, a lot of this is changing with the emergence of technology and AI in agriculture. Smart farming takes away the unpredictability and instability associated with agriculture making it easy for financial institutions to gather all the information they need so they can make informed lending decisions.

It helps financial institutions adopt a data-driven growth and expansion strategy

Lack of access to quality credit facilities and reliable banking continue to be some of the biggest challenges that limit smallholder farmers from getting financial assistance. Banks and other financial institutions struggle with bad loans and an increased burden of NPAs when it comes to agricultural lending. It results in making them unsure about their rural expansion strategy and thinking twice when it comes to agri lending operating outside cities. In the year 2013, about 52% of agricultural households in India were estimated to be indebted, with a large majority being attributed to banks. Since then, the government has taken multiple measures to reduce the risks associated with agricultural lending, with a large contribution from the adoption of technology in the sector.

The emergence of smart technology and AI in agriculture has led to the rise of a number of NBFCs that use technology for risk assessment and mitigation to offer credit to farmers. It enables financial institutions to understand more about growth and yield potential in different regions. Especially in agriculturally-driven economies like India, gaining reliable data-driven insights on such a large sector helps banks and financial institutions make informed expansion decisions. Real-time tracking through satellite imagery along with historical and weather performance data can help determine major agriculture zones so banks can effectively gauge their potential and tailor their products accordingly.

It improves overall efficiency and enables a smarter loan disbursal process

Using AI in agriculture, particularly in finance, banks to evaluate the credit and farming history of the farmers before offering loans. Smart farming technology benefits farmers and financial institutions as it improves transparency in the pre-disbursal process. With intelligence regarding the historical performance of the farms, along with real-time information about the yield potential for the current period, banks can determine the expected harvest from any plot and offer loans with reduced risk.

Technology allows banks to monitor and track funds on a real-time basis

The role of technology is not limited to identifying regions that would benefit from the credit, but it also helps in determining how well these funds are being utilised at the farm level. Traditionally, this involved sending field agents to manually inspect how well each region was performing. However, this is a time-consuming and unscientific method of data collection, which is far from accurate. Smart technology can help financial institutions keep track of the plots under cultivation on a real-time basis, which is not only beneficial to them but also helps farmers to have a stronghold on the health of their crops throughout their cultivation cycle.

The increased penetration of smartphones in rural areas has contributed significantly in bringing digital technology to farmers. A 2017 report by GSMA estimated that 295 million farmers out of 750 million across 69 countries own a mobile phone. This number is predicted to reach 350 million by the year 2020. Using mobile apps, farmers can maintain a record of every activity, right from seed selection and sowing to ongoing crop health assessment for timely harvesting, which helps track and monitor progress on a regular basis without deploying manual agents. This plot-level intelligence post loan disbursal reduces overall risk and ensures timely collection based on real-time on the ground information.

It helps in evaluating success and gauging branch performance by region

A high number of NPAs and increased chances of bad loans are major reasons for the reluctance of banks and financial institutions when it comes to offering loans to the sector. Before the coming of smart technology and AI in agriculture, banks had to rely on manual auditing by field agents to determine whether a farmer’s efforts would lead to a successful harvest and if he would be able to repay a loan. Digital technology offers a logical solution to this problem of uncertainty by providing accurate information at every stage of cultivation. It not only enables banks to reduce risks associated with farmer loans but also allows them to gauge the performance of their rural branches.

It needs to be understood that though there is an increase in the total credit allotted to agriculture over time, it has not helped in improving the accessibility to credit for farmers, especially in rural areas. The adoption of technology can help banks monitor and analyse activities of different branches to identify what is working and what needs improvement. Information such as the number of loans given by region, historical crop and growth data, , NPAs, and delinquencies in a region can help financial institutions refine their lending strategy and identify what they can do better to succeed in a given area or replicate the success of a region in other districts.

Discover more about how you can use AI in Agriculture here or find out how Cropin is using technology to help banks and other financial institutions offer suitable credit to farmers with minimal risk by reaching out to us.