Is India on the cusp of a new Agtech revolution?
Indian government has decided to make geospatial data and mapping technologies available to Indian business firms. The sweeping changes are reforming, and a trillion-dollar fintech opportunity might have been unleashed by our Prime Minister
According to India’s Prime Minister Narendra Modi, it will unlock enormous opportunities for India. The Department of Science and Technology while announcing changes said, “What is readily available globally does not need to be restricted in India and therefore geospatial data that used to be restricted will now be freely available in India.”
The Economic Times, quoted that, “India’s new mapping policy can be a game changer, pushing domestic firms to steal a march over global giants and tripling geospatial business valued at ₹30,000 crore now.”
However, I believe, no one has yet perceived that the Prime Minister has unknowingly unleashed a trillion-dollar fintech opportunity for the world and might have metamorphosed the credit system in India permanently.
The details of this change made is mentioned under 7(d) on page 2 of the final guidelines of geospatial data that says, “Map: Symbolic representation of real-world objects, regions or themes on a given scale which was generally published in paper form but now also available as web-map-service.”
Effectively India’s cadastral maps (authentic land ownership) are now a route for economic gains, and the law is squarely in favour of the commercial use. This can only mean more accurate underwriting for over a hundred million farmer families.
Speaking of the stats, under the Prime Ministers Jan Dhan Yojna (PMJDY) there are today 350 million accounts and about 176 million are from rural or semi-rural areas. And there are 66.2 million active Kisan Credit Card (KCC) holders in India. (The KCC accounts scheme, introduced in 1998, aims to provide crop loans to farmers).
To underwrite the loans for such a vast mass of population is a tedious process. Most rural folk will not have any exposure to formal lending beyond the KCC/micro finance institutions (MFIs). On contrary, MFI are typically non- agricultural loans especially to women, having interest more than 20%. Millions are dependent on informal credit markets where interest goes up 2% a day. And are hopelessly dependent on aadhat for credit (here social relations and connections forms basis of underwriting and eventually turns out to be very expensive).
So, what are the hard truths of rural lending? KCC has uneven linkage with CIBIL/CRIF, and if I am a farmer, beyond KCC, my cost of credit is at least 20% if not more. But if I am a salaried employee, because of the abundant data available for underwriting, I can get secured personal loan, home loan at 6.7% and personal unsecured loan at 9%.
The amalgamation of cadastral maps with high-resolution crop data overlaid with data about other assets such as cattle or any construction, automobile ownership and interlaced with data on power lines and market data creates virtual digital collateral for over 100 million families directly. And that automatically brings down the cost of underwriting, improving efficiency in KCC.
I think even accounting for inefficiencies, the cost of lending to rural India can be significantly brought down. At skAIgeo (Skymet’s new geospatial avatar) we have been working on virtual land record-based collateral. And finally, now are finding ourselves progressing amid a regulatory revolution.
Let me explain,
Presently the credit-score based system is not available for distribution of agriculture loans. And agricultural loans are not linked to consumption loans in any way.
Many states of India have ample fertile land, water, and congenial climate for agriculture cultivation, lacking institutional agriculture credit and digitalisation.
At skAIgeo, we have been building the digital platform in India for providing solutions for filling the gap between banks and farmers. The ‘digital lending solution’ provides farm score/ farmers financial health/repayment capacity using various parameters. We have decided to go for remote sensing through skAIgeo, Geo-tagging/fencing and overlaying cadastral maps over land records which helps in land and crop identification, crop acreage, and yield forecast. This solution is being used by SBI, ICICI Bank, HDFC Bank, and a few Maharashtra cooperative banks.
A land record based virtual collateral system does the following things.
• Assesses farm credit score based on crop health, farming history and credit history for hassle free loan disbursement.
• Integrates decision support system through which agri lending, crop health, weather forecast, and advisory services provided altogether.
• Scalable data for farmers who are outside the formal credit system.
A farmer does not have a formal financial track record of his farm expenditure, income and surplus. There is a general absence of credit history information and of credit reference bureaus in rural areas that collect and store information on borrowers.
The outcomes of the digital innovative solutions are reliable, hassle free loan disbursement, fraud detection, and a check over false land ownership and crops cultivated claims. Also, diversion of funds taken by farmers can also be investigated. Minimal manual intervention during loan processing is the added advantage.
We use remote sensing and AI/ML techniques to derive information from globally available datasets through Landsat, modus and sentinel to process images in stratified layers, each layer imparting unique information to acquire detailed knowledge about the field or crop.
At skAIgeo, we have thousands of in-house drones flying to execute crop survey and crop monitoring, facilitating the concept of intelligent farming, a robust digital farming technology.
As the systems get robust, the virtual collateral can be made available for other kinds of lending. The opening up of maps are going to end leakages and allow banks to underwrite more effectively. The cost of credit in rural India should now come down. And that is hopefully geoing to be a huge opportunity.
Source – Rohit Singh firstname.lastname@example.org