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An Alternative Credit Scoring Model  by CreDA in Brazil

This week as CreDA kicked off a first pilot program in Brazil to provide an alternative credit scoring model that delivers competitive, affordable interest rates to farmers in the country.


We are working with farmers across Brazil including, Alta Vista, Barreiras, Bahia & Mirante Santana, and Aguas Da Prata, near São Paulo to analyse Satellite, environmental and historic usage data in order to understand the farm size, crop conditions, irrigation, and yield potential. This data is then modelled with CreDA’s proprietary credit risk algorithms that assess assets and payment behaviour both in the traditional system and on blockchain. This credit score could slash up to 50% of the eye watering interest rates that Brazilian farmers have been obliged to pay.
In partnering with Ager Solution, a company that works with farmers to help increase efficiency of farms by giving high-quality insights on the needs of their crops the project aims to create a metaverse that uses data and an augmented reality to change the game.


Ager utilizes satellite data and NDVI imaging in order to create 17 layers of metadata that can be used to give in-depth analysis of the agricultural field. This data will be incorporated with CreDA’s data to provide a robust, unbiased and real-time view of a farm’s value and risk profile.

Farmers in Brazil want to contribute to the country’s food security but are faced with unrealistic interest rates for loans needed to expand their farms because the lack of adequate data means banks won’t take the risk the so called “Credit Paradox” So the Metaverse approach is all about putting power back in the hands of the farmers, meaning even smaller scale operations could have the funds needed to cover the upfront costs of farming, including seeds, equipment, labour, fertilizer, and much more.

We are already underway with the data collection and will be sharing more details about the project in the coming weeks. Already one learning from the analysis is a requirement to engage sustainability mechanism into the analysis to further strengthen the reality of the model.

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