Researchers Submit Patent Application, “Method And System For Assessing Farmer Credit Worthiness And Risk Associated With Farm”, for Approval (USPTO 20230064592): Tata Consultancy Services Limited

2023 MAR 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors MOHAN, SANTOSH KUMAR (Chennai, IN); MOHITE, JAYANTRAO (Thane, IN); PAPPULA, SRINIVASU (Hyderabad, IN); SAKKAN, MARIAPPAN (Chennai, IN); SARANGI, SANAT (Thane, IN); SAWANT, SURYAKANT ASHOK (Thane, IN); SIVALINGAM, RAVINKUMAR (Chennai, IN), filed on May 17, 2022, was made available online on March 2, 2023.

The patent’s assignee is Tata Consultancy Services Limited (Mumbai, India).

News editors obtained the following quote from the background information supplied by the inventors: “Agriculture is a dominant sector for economies of several nations and credit plays an important role in increasing agriculture production. Availability and access to adequate, timely and low-cost credit from institutional sources is of great importance especially to small and marginal farmers. The lending institutions need to ensure that the farmer to which they providing the loans are credit worthy enough to repay the loan amount.

“The agricultural lending decision making process is complex due to contractual and ownership arrangement issues, locational issues, and management quality and risk management issues. Credit risk is the largest risk faced by banks and financial institutions in agricultural loan, most of the financial institutions relied on subjective analysis or the so-called banker expert system to assess the credit risk of borrowers, however this method may be inconsistent in risk assessment and determination of credit worthiness. The degree of competition in agricultural lending will influence quantity and quality of loans made by the lending institutions.

“Various methods have been used in the past to assess the credit worthiness of the farmer. Traditional cum banker’s expert models may be inconsistent is assessing credit worthiness of farm, farmer in agricultural lending agency. No robust method in place to assess the farm credit risk associated with farm and farmer entrepreneurial quality on real time basis. Banker expert models relying on physical datasets, with manual intervention is not scalable and fail due to low reliability and accuracy. Another approach is the calculation of farm credit risk score, which is indicative of the credit worthiness of the farmer and risk associated with the farm, but even this approach has not been explored much.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a system for assessing credit worthiness of a farmer and risks associated with a farm is provided. The system comprises one or more satellites, an input/output interface, one or more hardware processors and a memory. The one or more satellites receives a remote satellite data sensed over a predefined period over the farm. The memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the one or more first memories, to: generate a farm resource map using the remote satellite data; obtain an agro-climatic (AC) zone data respective to the farm from a data repository; calculate a Know Your Customer Farm (KYCF) score using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm; receive a set of socioeconomic parameters and a set of socio-personal parameters of the farmer via a questionnaire, wherein the questionnaire is filled up by the farmer; calculate a Know Your Farmer (KYF) score for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer; collect a first set of parameters from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer; calculate a Farmer entrepreneurship Quality (FEQ) score using the first set of parameters, wherein the FEQ score is indicative of the ability of the farmer to repay the loan; calculate a farm credit risk score in real time using the KYCF score, the KYF score and the FEQ score; and compare the calculated farm credit risk score with a predefined criterion to classify the credit worthiness of the farmer and risk associated with the farm as one of a high risk, a moderate risk or a low risk.

“In another aspect, a method for assessing credit worthiness of the farmer and risks associated with a farm is provided. Initially, a remote satellite data sensed from one or more satellites over a predefined period over the farm is received. Further, a farm resource map is generated using the remote satellite data. In the next step, an agro-climatic (AC) zone data respective to the farm is obtained from a data repository. Further, a Know Your Customer Farm (KYCF) score is calculated using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm. Later a set of socioeconomic parameters and a set of socio-personal parameters of the farmer are received via a questionnaire, wherein the questionnaire is filled up by the farmer. In the next step, a Know Your Farmer (KYF) score is calculated for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer. Further, a first set of parameters are collected from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer. In the next step, a Farmer entrepreneurship Quality (FEQ) score is calculated using the first set of parameters, wherein the FEQ score is indicative of the ability of the farmer to repay the loan. A farm credit risk score is then calculated in real time using the KYCF score, the KYF score and the FEQ score. And finally, the calculated farm credit risk score is compared with a predefined criterion to classify the credit worthiness of the farmer and risk associated with the farm as one of a high risk, a medium risk or a low risk.

“In yet another aspect, one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause assessing credit worthiness of the farmer and risk associated with a farm is provided. Initially, a remote satellite data sensed from one or more satellites over a predefined period over the farm is received. Further, a farm resource map is generated using the remote satellite data. In the next step, an agro-climatic (AC) zone data respective to the farm is obtained from a data repository. Further, a Know Your Customer Farm (KYCF) score is calculated using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm. Later a set of socioeconomic parameters and a set of socio-personal parameters of the farmer are received via a questionnaire, wherein the questionnaire is filled up by the farmer. In the next step, a Know Your Farmer (KYF) score is calculated for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer. Further, a first set of parameters are collected from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer. In the next step, a Farmer entrepreneurship Quality (FEQ) score is calculated using the first set of parameters, wherein the FEQ score is indicative of the ability of the farmer to repay the loan. A farm credit risk score is then calculated in real time using the KYCF score, the KYF score and the FEQ score. And finally, the calculated farm credit risk score is compared with a predefined criterion to classify the credit worthiness of the farmer and risk associated with the farm as one of a high risk, a medium risk or a low risk.

“It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.”

The claims supplied by the inventors are:

“1. A processor implemented method for assessing credit worthiness of a farmer and risks associated with a farm, the method comprising: receiving a remote satellite data sensed from one or more satellites over a predefined period over the farm; generating, via one or more hardware processors, a farm resource map using the remote satellite data; obtaining, via the one or more hardware processors, an agro-climatic (AC) zone data respective to the farm from a data repository; calculating, via the one or more hardware processors, a Know Your Customer Farm (KYCF) score using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm; receiving, via the one or more hardware processors, a set of socioeconomic parameters and a set of socio-personal parameters of the farmer via a questionnaire, wherein the questionnaire is filled up by the farmer; calculating, via the one or more hardware processors, a Know Your Farmer (KYF) score for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer; collecting, via the one or more hardware processors, a first set of parameters from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer; calculating, via the one or more hardware processors, a Farmer entrepreneurship Quality (FEQ) score using the first set of parameters, wherein the FEQ score is indicative of ability of the farmer to repay the loan; calculating, via the one or more hardware processors, a farm credit risk score in real time using the KYCF score, the KYF score and the FEQ score; and comparing, via the one or more hardware processors, the calculated farm credit risk score with a predefined criterion to classify the credit worthiness of the farmer and risks associated with the farm as one of a high risk, a medium risk or a low risk.

“2. The method of claim 1, wherein the set of socio-personal parameters comprises family size of the farmer, family type of the farmer, educational qualification of the farmer, primary occupation of the farmer, secondary occupation of the farmer, women member in the family and farming experience.

“3. The method of claim 1 further comprising recommending a personalized crop protocol depending on the classified risk.

“4. The method of claim 1, wherein the set of socioeconomic parameters comprises a set phone model, economic parameters of the farmer comprising cash flow, annual income from agriculture, annual income from non-agricultural activities, type of loan availed, loan due amount and loan default.

“5. The method of claim 1 wherein the one or more satellites comprises Sentinel-1, Sentinel-2, Landsat-5, Landsat-6, and Landsat-7.

“6. A system for assessing credit worthiness of a farmer and risk associated with a farm, the system comprises: one or more satellites for receiving a remote satellite data sensed over a predefined period over the farm; an input/output interface; one or more hardware processors; and a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the one or more first memories, to: generate a farm resource map using the remote satellite data; obtain an agro-climatic (AC) zone data respective to the farm from a data repository; calculate a Know Your Customer Farm (KYCF) score using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm; receive a set of socioeconomic parameters and a set of socio-personal parameters of the farmer via a questionnaire, wherein the questionnaire is filled up by the farmer; calculate a Know Your Farmer (KYF) score for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer; collect a first set of parameters from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer; calculate a Farmer entrepreneurship Quality (FEQ) score using the first set of parameters, wherein the FEQ score is indicative of ability of the farmer to repay the loan; calculate a farm credit risk score in real time using the KYCF score, the KYF score and the FEQ score; and compare the calculated farm credit risk score with a predefined criterion to classify the credit worthiness of the farmer and risk associated with the farm as one of a high risk, a moderate risk or a low risk.

“7. The system of claim 6, wherein the set of socio-personal parameters comprises family size of the farmer, family type of the farmer, educational qualification of the farmer, primary occupation of the farmer, secondary occupation of the farmer, women member in the family and farming experience.

“8. The system of claim 6 further configured to provide a recommendation to follow a personalized crop protocol depending on the classified risk.

“9. The system of claim 6, wherein the set of socioeconomic parameters comprises a set phone model, economic parameters of the farmer comprising cash flow, annual income from agriculture, annual income from non-agricultural activities, type of loan availed, loan due amount and loan default.

“10. The system of claim 6, wherein the one or more satellites comprises Sentinel-1, Sentinel-2, Landsat-5, Landsat-6, and Landsat-7.

“11. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving a remote satellite data sensed from one or more satellites over a predefined period over the farm; generating, a farm resource map using the remote satellite data; obtaining, via the one or more hardware processors, an agro-climatic (AC) zone data respective to the farm from a data repository; calculating, via the one or more hardware processors, a Know Your Customer Farm (KYCF) score using the farm resource map and the AC zone data, wherein the KYCF is indicative of quality of the farm; receiving, via the one or more hardware processors, a set of socioeconomic parameters and a set of socio-personal parameters of the farmer via a questionnaire, wherein the questionnaire is filled up by the farmer; calculating, via the one or more hardware processors, a Know Your Farmer (KYF) score for the farmer using the set of socioeconomic parameters and the set of socio-personal parameters, wherein the KYF score indicative of the financial worthiness of the farmer; collecting, via the one or more hardware processors, a first set of parameters from a plurality of sources, wherein the first set of parameters comprises cropping pattern of the farming over a predefined time period, social participation of the farmer, experience of farming, experience in banking, information seeking behaviour of the farmer, marketing behaviour of the farmer and a farming knowledge of the farmer based on a number of trainings attended by the farmer; calculating, via the one or more hardware processors, a Farmer entrepreneurship Quality (FEQ) score using the first set of parameters, wherein the FEQ score is indicative of ability of the farmer to repay the loan; calculating, via the one or more hardware processors, a farm credit risk score in real time using the KYCF score, the KYF score and the FEQ score; and comparing, via the one or more hardware processors, the calculated farm credit risk score with a predefined criterion to classify the credit worthiness of the farmer and risks associated with the farm as one of a high risk, a medium risk or a low risk.

“12. The one or more non-transitory machine-readable information storage mediums of claim 11, wherein the set of socio-personal parameters comprises family size of the farmer, family type of the farmer, educational qualification of the farmer, primary occupation of the farmer, secondary occupation of the farmer, women member in the family and farming experience.

“13. The one or more non-transitory machine-readable information storage mediums of claim 11 further comprising recommending a personalized crop protocol depending on the classified risk.

“14. The one or more non-transitory machine-readable information storage mediums of claim 11, wherein the set of socioeconomic parameters comprises a set phone model, economic parameters of the farmer comprising cash flow, annual income from agriculture, annual income from non-agricultural activities, type of loan availed, loan due amount and loan default.

“15. The one or more non-transitory machine-readable information storage mediums of claim 11 wherein the one or more satellites comprises Sentinel-1, Sentinel-2, Landsat-5, Landsat-6, and Landsat-7.”

For additional information on this patent application, see: MOHAN, SANTOSH KUMAR; MOHITE, JAYANTRAO; PAPPULA, SRINIVASU; SAKKAN, MARIAPPAN; SARANGI, SANAT; SAWANT, SURYAKANT ASHOK; SIVALINGAM, RAVINKUMAR. Method And System For Assessing Farmer Credit Worthiness And Risk Associated With Farm. U.S. Patent Application Number 20230064592, filed May 17, 2022 and posted March 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230064592)&db=US-PGPUB&type=ids

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