26 Sep 2022 — Credit DeFiAlliance (CreDA) has launched an alternative credit scoring model for farmers in Brazil. The decentralized credit scoring service has built an artificial intelligence (AI) service that scores farmers’ farm size, crop conditions, irrigation and yield potential, and provides a rate based on the score.
This system could cut up to 50% of the interest rates Brazilian farmers have to pay.
Interest rates in Brazil have been tightened by the war against Ukraine and the pandemic. The conflicts have caused inflation in the country, and to curb it, interest rates have risen over time.
These rising rates have pushed up the cost of food, fuel and fertilizer, making it more expensive for farmers to get the job done. In addition, high interest rates do not allow farmers easy access to money to invest in their crops, which in turn affects the food supply.
Deflating interest rates with AI
CreDA aims to offer affordable interest rates to farmers in Brazil. This is done as part of the company’s Credit4Good program.
The program merges on- and off-chain data to create custom, objective credit risk models that allow farmers to access an additional line of credit at a better and more competitive price.
“In short, Credit4Good allows them to bypass the traditional means of accessing capital, enables them to run their business and helps feed more Brazilians,” says Fredrico Diniz, head of credit risk modeling at CreDA.
CreDA has developed a “fit for purpose” model that uses proprietary credit risk algorithms that score assets and payment behaviors on both the traditional system and the blockchain.
“The model is not about creating new assets that can offset loans. It’s about incorporating the existing, intrinsic essence of a farm – from the quality of the soil to its past performance – into the credit assessment process,” explains Diniz.
Due to limited competition, Brazil’s top five banks currently manage 80% of the country’s loans. According to CountryEconomy, interest rates were at 4.50% immediately before the pandemic, while they are now at 13.75% and rising.
Powered by machine learning
To develop the technology needed to evaluate farmers’ interest rates, CreDA partnered with Ager Solution. This company helps increase farm efficiency by providing insight into the needs of their crops.
Their farmer rating system integrates AI as part of the so-called CreDA Oracle. This machine learning algorithm has been trained on billions of activities in the chain related to more than 100 million addresses.
The company also uses satellite data and NDVI imaging – which measures the difference between visible and near-infrared light reflectance from vegetation to create images of photosynthetic power – to create 17 layers of metadata. CreDA will integrate this data with its own to provide complete and real-time views of a farm’s value and risk profiles.
When it comes to cryptocurrency, users can assess CreDA’s personal credit score on the company’s website and use it as a CreditNFT, ensuring that the score is linked to the person. The score can also be adjusted daily by the user’s behavior in the crypto and Web3 ecosystem.
“In the future, it will also bring other considerations into the mix, such as environmental considerations, participation in shared materials or cooperatives, or local community involvement, all of which could directly affect a farmer’s creditworthiness,” claims Diniz.
Global Food Crisis
According to the World Food Program, an estimated 828 million people face hunger every day. In the past two years, numbers have risen from 135 million in 53 countries before the pandemic to 345 million in 82 countries today.
“Farmers in Brazil want to contribute to the country’s food security, but face unrealistic interest rates on loans needed to expand their farms because banks do not take the risk due to a lack of sufficient data,” says Diniz.
Brazil and the greater part of South America are also facing coffee bean shortages, and bean farmers are holding on to their stockpiles to push up prices. More volatility is expected in the Brazilian food market.
Edited by Sabine Waldeck
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