The present invention relates to a system and method for evaluation of income and credit analysis and scoring of persons with undocumented income mostly in the unorganised sectors. The present invention, in particular, relates to the field of personal banking and financial services and provides a mechanism to evaluate income and credit analysis and score based on a surrogate model using Deeptech algorithms for individuals belonging to the marginalized, unorganized and undocumented income group.

BACKGROUND AND PRIOR ART OF THE INVENTION
In the third world countries like India, we have more than 40 percent of the earning population which has unstructured and undocumented income i.e. they do not have any kind of receipts, salary slips, P&L statements etc. which can be accepted as a proof of their income e.g. farmers, daily laborers, maids, drivers, micro entrepreneurs, gig workers etc. They are mostly into cash transactions. Most of these households operate in informal economic segment. They are not eligible for institutional financial assistance. Since their incomes are undocumented, they do not have any financial identity, credit score etc. to enable them to obtain financial loans and other instruments like credit cards, insurance etc. from the institutions in formal economy like Banks, NBFCs, Insurance etc.
Insurance companies are reluctant to underwrite life, health or accidental insurance etc. for otherwise eligible amount in absence of the reliable income estimate.
However today most of them have a proof of identity (Aadhaar) and a Jan Dhan bank account, Mobile (JAM trinity). All they require is a “Financial Identity”.
US20150213562A1 discloses a method and apparatus for providing a “personal credit analysis and values” for an individual. The method collects the data associated with the user like his personal information, income , expenses ,assets, liabilities , skills ,health ,family, social connection, psychometric, credit data from rating agencies like CIBIL etc. It then enhances the data that is collected , then analyze and rates the data using a deeptech based analysis and rating engine , and then generates credit analysis and rating for an individual as a response to the request & provides “personal credit analysis and value” for the user.
Almost all the existing credit scoring algorithm uses the past transactions data or credit history to arrive at credit scoring which is a direct model. Unfortunately for people with undocumented income , credit analysis and scoring is not possible by using the existing methodology. Therefore, there is need to develop new techniques to assess and verify the income and the financial capability for undocumented income group through a surrogate indirect model.
The present invention relates to a system and method for a Deeptech based surrogate credit analysis and rating management to provide financial identity through a digital platform to recommend a credit decision for the financial institutions and for underwriting by the insurance companies.
OBJECT OF THE INVENTION
The various embodiments of the present invention are to provide a Deep-tech based system and method for evaluating financial identity, income and credit analysis and scoring for the persons belonging to the marginalized, un-organized and undocumented income group.
The main objective of the present invention is to evaluate the financial capability of a person making use of socio-economic statistics.
Another object of the present invention is to evaluate the financial capability of a person from formal and informal investments.
Yet another object of the present invention is to evaluate the financial capability of a person from previous direct or indirect income.
Yet another object of the present invention is to evaluate the financial capability of a person from social records.
Yet another object of the present invention is to evaluate the financial capability of a person from financial records and service availed.
Yet another object of the present invention is to evaluate the financial capability of a person from psychometric data.
Yet another object of the present invention is to evaluate the financial capability of a person from expenditure information.
Yet another object of the present invention is to evaluate the financial capability of a person from is and family health status.
Yet another object of the present invention is to evaluate the financial capability of a person from qualifications , Skills. & expertise.
Yet another object of the present invention is to evaluate the financial capability of a person from assets records.
Yet another object of the present invention is to assess the propensity to repay.
Yet another object of the present invention is to provide a credit score evaluation system to be used for the persons in the formal income group .
SUMMARY OF THE INVENTION
The present invention relates to a Deep-tech based credit analysis and rating management system and method to evaluate the income and credit score of the individuals from the undocumented income group.
The present invention relates to a system for evaluation of income and credit score of persons with undocumented income consisting of a collection module, an assessment module, a processing module, an encryption and storage module and optionally a risk management module, wherein ;
- the collection module collects data regarding financial capability and plurality of other data of an individual;
- the assessment module validates the social & behavioral attributes of the individual;
- the processing module processes credit analysis and score for the individual based on the financial capability and social & behavioral attributes;
- the encryption and storage module encrypts and saves the collected and processed data in a remote database; and
- the risk management module provides a risk management solution based on the credit score generated for the financial institution.
In an embodiment the plurality of other data includes identity data, family data, asset data, socio-economic data, medical data and liability data.
In an embodiment the processing module works on a Deeptech based algorithm.
In another embodiment the processing module works on Neural Network algorithm.
In an embodiment, the processing module converts the data from collection module and assessment module into plurality of parameters, ßi, based on a Deeptech Algorithm.
In an embodiment, the plurality of parameters, ßi, is used to calculate loan eligibility by the function :
Loan Eligibilityi = a + ß1 Income from Wagesi + ß2 Income from Cultivationi + ß3 Income from Live Stocki + ß4 Income from Non-farm businessi + ß5 Expenditure on Foodi + ß6 Expenditure on Healthi + ß7 Expenditure on Clothesi + ß8 Expenditure on Educationi + ß9 Expenditure on Servicesi + ß10Land/House Holdingi + ß11 Gold and Silveri + ß12 Transport Equipmenti + ß13 Agriculture Machineryi + ß14 Non-Farm Business Equipmenti + ß15 Investment in govt. bonds/Insurancei + ß16 Deposits with MFI/SHG/CFi + ß17 Family Sizei + ß18 Number of Dependentsi + ß19 Number of Earning Membersi +ß20 Alcohol Consumption smoking or any other contraband consumptioni + ß21Agei + ß22Educationi + ß23Health status/TBi + ui, where,
i= 1, 2,3,……..,n and ui~ N(µ, s2)
Where a, ß1 …. ß23 are the coefficients.
In an embodiment, the Deep-tech algorithm is self-learning and the parameters are updated to precision on every evaluation.
Another embodiment of the system and method for evaluating the credit score, performs the calculation using Deep Neural Network model considering the following function:
Loan Eligibility = f ( Income from Wages, Income from Cultivation, Income from Live Stock, Income from Non-farm business, Expenditure on Food, Expenditure on Health, Expenditure on Clothes, Expenditure on Education, Expenditure on Services, Land/House Holding, Gold and Silver, Transport Equipment, Agriculture Machinery, Non-Farm Business Equipment, Investment in govt. bonds/Insurance, Deposits with MFI/SHG/CF, Family Size, Number of Dependents, Number of Earning Members, Alcohol Consumption smoking or any other contraband consumption, Age, Education, Health status/TB)
Another embodiment of the present invention relates to a system to evaluate the income and credit analysis of the undocumented income group comprising of a collection module, an assessment module, a processing module ,
wherein,
- the collection module collects the information regarding personal, financial, social, behavioral, assets, income, expenditure, health, family, psychometric attributes of the individual;
- the assessment module validates the dataset with a learning process from statistical dataset;
- the processing module process a credit analysis and score for the individual based on the above attributes.
The present invention also relates to a computer implemented method to evaluate the income and credit score comprising of :
- a step of profile creation,
- a step of evaluation of income by analysing plurality of evaluation parameters,
- a step of estimation of income and probability of default , and
- a step of processing the credit analysis score, Income and Probability of default based on a Deeptech algorithm.
In an embodiment of the proposed invention, the step of profile creation consists of collecting plurality of data of the customer selected from personal data, financial data, socio-economic data, and medical data, social data, psychometric questionnaire.
In an embodiment, the steps of evaluation of income, calculation of probability of default and processing of credit score is based on Artificial Intelligence/Deep-tech based algorithm.
In an embodiment, the evaluation of income, probability of default and credit score is done by the processing unit.
In an embodiment, the plurality of predefined parameters is evaluated based on personal attributes, socio- economic attributes, psychological attributes, and medical data.
An embodiment of the present invention also relates to a computer implemented method to evaluate the income and credit score of the undocumented income group comprising of a step of profile creation, a step of evaluation of income by analysing a set of evaluation parameters based on a Deeptech algorithm, a step of estimation of probability of default based on the Deeptech algorithm, a step of processing the credit analysis score, of Probability of Income and Probability of default
wherein;
the step of profile creation includes making an entry of the individual, in a remote database with the identity proof, address proof, skills, prevailing rates in his state of residence and locality ;
the step of evaluation of income by analysing a set of financial and sociological parameters; wherein,
the financial parameters are selected from financial details such as income from salary and indirect income, assets, Investments (Formal and Informal), liabilities and loans (direct & indirect), formal Financial records if any (Bank a/c and PAN); and
the sociological parameters include family constitution (earning and non-earning members), Medical history of the individual and family members, Personal habits (like Alcohol and Tobacco consumption, Expenditure (Food, Non-Food, Education, Medical); and psychological parameters;
the step of estimation of probability of defaults, is done by analysing the financial and Social behaviour (Type of friends and family in connect) and Psychometric capability, based on AI algorithm to assess the intent and integrity to (propensity) to repay;
the step of processing the credit analysis and score is done by analysing the financial parameters, sociological parameters and psychometric parameters to formulate a credit score.
In an embodiment, the credit analytics by the proposed invention is based on AI surrogate data.
In an embodiment, the proposed invention also estimates the probability of default.
In an embodiment, the proposed invention also estimates the Probability of willful default by a customer.
In an embodiment, the proposed system and method provides the estimation of income of a customer based on AI surrogate data.

BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 shows the system (100) of evaluation of income and credit score. The system receives information from the customer (110). Plurality of other information is received from external servers and databases for personal details (120), information regarding his financial details (130) medical history (140), details of psychometric analysis (150), socio-economic background (160), details of social data (170) etc. The collection module (101) collects all the information. The assessment module (102) validates the social & behavioural dataset, the processing module (103) evaluates the income, credit score and probability of default, the risk and assessment module (104) provides the risk management solution and the encryption & storage module (105) encrypts and save the collected and processed data in the remote database (190).
Fig 2. shows the flowchart of the method (200) of evaluation of income and credit score. The method involves step of profile creation (201), a step of evaluation of income (202), a step of estimation of income and probability of default (203), and a step of processing the credit analytics and Probability of default (204).
Fig. 3 shows the work flow (300) of the system (100) implementing the method (200). The Loan app and AI credit engine and Psychometric engine are the integrated components of the system (100).

DETAILED DESCRIPTION OF THE INVENTION
The proposed invention relates to a system (100) and method (200) for evaluation of income and credit analytics and score for the persons with undocumented income.
The proposed invention relates generally to a system (100) and method (200) for a Deep-tech based surrogate credit analysis and rating management system to provide Financial Identity through a digital platform to recommend a credit decision for the financial institutions and underwriting by the insurance companies.
The proposed invention provides a new technique to assess the income and financial capability for undocumented income group through a surrogate indirect model.
The proposed system (100) consists of a collection module(101), an assessment module (102), and a processing module (103) , an encryption and storage module(105), and a risk management module (104),
wherein,
- the collection module (101) collects the information regarding financial capability and other data of an individual;
- the assessment module (102) validates the social & behavioral attributes of the individual;
- the processing module (103) process a credit analytics and score for the individual based on the financial capability and social & behavioral attributes;
- the encryption & storage module (105) stores all the user defined data and the processed data in an encrypted format and stores in on a database (190) on a remote server;
- the risk management module (104) provides a risk management solution based on the credit score generated for the financial institution (195).
The credit engine learning and assessment of the financial capability of a person, which is built from dataset using the socio-economic statistics collected at the National Level (e.g National Sample Survey (NSSO)) and other data. NSSO data covers socio-economic statistical data about India , like Employment and Unemployment, domestic tourism, housing condition , sanitation, Drinking water, Land and livestock holdings, Social consumption, health, Domestic tourism expenditure, Labour Force, Construction, Industries, Manufacture, etc
When a person from undocumented income group approaches a financial institution (195), the collection module (101) will collect relevant data from various sources such as proof of identity, proof of address, income declaration direct and derived, Skills, prevailing rates in that state and locality, assets, expenditure (Food, Non Food, Education, Medical), Family details (Earning and nonearning members, member with chronic disease), Personal habits like Alcohol and Tobacco consumption, Age groups, Investments (Formal and Informal), previous loans (Direct and indirect), Social (Type of friends and family in connect), formal Financial records if any (Bank a/c and PAN), Psychometric questions to assess the intent and integrity to( propensity to repay) predict the financial capability and credit scoring of individuals.
The proposed system and method can also estimate the income of a person from the undocumented income group and assess the probability of default including the probability of wilful default based on AI surrogate data.
The collected data is utilized by the Deep-tech /Neural network algorithms with predictive analytics wherein the AI engine continuously improves the accuracy of weightage and results.
The system uses AI algorithms to
a) Validate the income of the persons from the undocumented income group
b) Estimate the loan/ Insurance eligibility amount
c) Predict probability of default and periodicity of follow up actions required including collection (daily, weekly, monthly, quarterly or with cropping cycle) to avoid or minimize the default.
The system comprising the collection module (101), assessment module (102), processing module (103), encryption & storage module (105) and risk management module (104) will provide a solution to evaluate the credit score for an individual & risk management protocol to be followed by the financial institution(195) to recover the loan from the individual.
The proposed invention also relates to a method (200) of evaluating the income and credit score of marginalised income group consisting of steps of profile creation (201), step of evaluation (202) of income by analysing a set of evaluation parameters, step of estimation (203) of probability of default based on the AI algorithm, Step of processing (204) the credit score and recommending the risk management protocol to the financial institution(195).
An embodiment of the work-flow (300) of the proposed system (100) implementing the proposed method (200) is shown in Fig.3. The person approaches a financial institution (195) e.g bank or a financial establishment for loan. The loan app checks the credentials of the person and in case of existing customers, the details, i.e. name, address, Pan Details, Aadhaar card details, existing loans, previous history of loans etc., are sent to the System (100) for evaluation of credit score. The loan app is the integrated version of the collection module (101) & assessment module (102). The processing module (103) consists of AI credit Engine and Psychometric Engine. The system (100) communicates with the regulatory authorities (CIBIL/Equifax) database to fetch information regarding the customer. The system (100) evaluates the credit score based on AI integrated credit evaluation mechanism and processes the received information with Psychometric analysis results to provide a result comprising credit score & probability of default.
In case of a new customer, the system (100) collects all details from the customer (110) and verifies the authenticity of the information and creates a profile. The system (100) performs e-verification (e-kyc) of the data provided by the new customer. The assessment module fetches other information regarding the socio-economic status, employability of customer, data from NSSO and the details of his psychometric analysis and the processing unit (comprising the AI credit Engine and Psychometric engine) evaluates the income, credit score and the probability of default of the customer. Based on this result the financial institutions can decide on the loan (based on credit score) & formulate a strategy for risk management (based on probability of default).
The data is collected from the individual regarding his income, financial attributes, social attributes, psychometric attributes etc and is kept in an encrypted format on a database on the remote server. The ML/AI algorithm performs data analytics and calculates the credit score to be followed by the financial institution (195) for realising loan repayment.
In an embodiment, the system and method for evaluating the credit score, performs the calculation using Multiple regression model considering the following equation:
Loan Eligibilityi = a + ß1 Income from Wagesi + ß2 Income from Cultivationi + ß3 Income from Live Stocki + ß4 Income from Non-farm businessi + ß5 Expenditure on Foodi + ß6 Expenditure on Healthi + ß7 Expenditure on Clothesi + ß8 Expenditure on Educationi + ß9 Expenditure on Servicesi + ß10Land/House Holdingi + ß11 Gold and Silveri + ß12 Transport Equipmenti + ß13 Agriculture Machineryi + ß14 Non-Farm Business Equipmenti + ß15 Investment in govt. bonds/Insurancei + ß16 Deposits with MFI/SHG/CFi + ß17 Family Sizei + ß18 Number of Dependentsi + ß19 Number of Earning Membersi +ß20 Alcohol Consumption smoking or any other contraband consumptioni + ß21Agei + ß22Educationi + ß23Health status/TBi + ui
i= 1, 2,3,……..,n and ui~ N(µ, s2)
Where a, ß1 …. ß23 are the coefficients
wherein, the data for each variable is fetched from large scale sample survey at the all-India level like National Sample Survey, stored in remote databases.
An embodiment of the system and method for evaluating the credit score, performs the calculation using Deep Neural Network model considering the following function:
Loan Eligibility = f ( Income from Wages, Income from Cultivation, Income from Live Stock, Income from Non-farm business, Expenditure on Food, Expenditure on Health, Expenditure on Clothes, Expenditure on Education, Expenditure on Services, Land/House Holding, Gold and Silver, Transport Equipment, Agriculture Machinery, Non-Farm Business Equipment, Investment in govt. bonds/Insurance, Deposits with MFI/SHG/CF, Family Size, Number of Dependents, Number of Earning Members, Alcohol Consumption smoking or any other contraband consumption, Age, Education, Health status/TB)
The system (100) assesses the probability of default (PD) from direct and indirect sources. Probability of default (PD) is a financial term describing the likelihood of a default over a particular time horizon. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations.
The credit score is calculated using algorithms such as logistic regression, random forest, decision tree, Support Vector Machine (SVM), Neural networks or any other classification algorithm depending on suitability of the model. The variables be used for estimation of probability of default, loan eligibility amount and income estimation, are selected from the following innovative parameters:
1. Income from Wages
2. Income from Cultivation
3. Income from Live Stock
4. Income from No n-farm business
5. Expenditure on Food
6. Expenditure on Health
7. Expenditure on Clothes
8. Expenditure on Education
9. Expenditure on Services
10. Land/House Holding
11. Gold /Silver
12. Transport Equipment
13. Agriculture Machinery
14. Non-Farm Business Equipment
15. Investment in govt. bonds/Insurance
16. Deposits with MFI/SHG/CF
17. Family Size
18. Number of Dependents
19. Number of Earning Members
20. Alcohol Consumption, smoking or any other contraband consumption.
21. Age
22. Education
23. Health status like TB or other chronic diseases
The risk management solution proposed by the system is based on the calculated credit score, estimated propensity of repayment, Relevance of skills over a period and job opportunities; Ability to relocate and change, Characters of friends and families, Criminal records of the individual, Psychometric test results.
In an embodiment the system (100) is a computing device executing software instructions, interacting with different databases located on remote locations/cloud, interacting with different servers for obtaining data from other networks/sources, for authentication and validation and executing a method to evaluate the credit score for an customer based on various inputs received from the said databases/servers/sources.
The computing device may include, desktop computers, laptop computers, mobile phones, smart phones, tablets or any communication device.
The system may communicate with other computing devices/ databases/servers/application servers through internet, wireless network, wide area network (WLAN), Local Area Network (LAN) or any combination thereof.
The description and drawing only illustrate embodiments of the present invention and should not be construed in limiting the scope of the invention.

ADVANTAGES
The proposed invention has the following advantages over existing systems :
• It provides a system and method for the evaluation of income and credit analysis and score for the persons belonging to the marginalized income group, which was practically non-existent.
• It provides system and method for the evaluation of income and credit scoring for the persons belonging to the unstructured and undocumented income group which is difficult to evaluate.
• It provides a solution to formulate a risk management protocol for financial institutions based on AI algorithms.
• The AI engines learn continuously and improves the accuracy of weightage and results.

We Claim :

1. A system (100) for evaluation of income and credit score of persons with undocumented income consisting of a collection module (101), an assessment module (102), a processing module (103), an encryption and storage module (105) and optionally a risk management module (104), wherein ;
- the collection module (101) collects data regarding financial capability and plurality of other data of an individual;
- the assessment module (102) validates the social & behavioral attributes of the individual;
- the processing module (103) processes credit analysis and credit score for the individual based on the financial capability and social & behavioral attributes;
- the encryption and storage module (105) encrypts and saves the collected and processed data in a remote database (190); and
- the risk management module (104) provides a risk management solution based on the credit score generated for the financial institution (195).
2. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the plurality of other data includes identity data, family data, asset data, socio-economic data, medical data and liability data .
3. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the processing module (103) works on a Deeptech /Neural Network based algorithm.
4. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the processing module (103) converts the data from collection module (101) and assessment module into plurality of parameters, ßi, based on the Deeptech /Neural network Algorithm.
5. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the loan eligibility is calculated by the function : Loan Eligibility = f ( Income from Wages, Income from Cultivation, Income from Live Stock, Income from Non-farm business, Expenditure on Food, Expenditure on Health, Expenditure on Clothes, Expenditure on Education, Expenditure on Services, Land/House Holding, Gold and Silver, Transport Equipment, Agriculture Machinery, Non-Farm Business Equipment, Investment in govt. bonds/Insurance, Deposits with MFI/SHG/CF, Family Size, Number of Dependents, Number of Earning Members, Alcohol Consumption smoking or any other contraband consumption, Age, Education, Health status/TB).
6. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the credit analytics is based on AI surrogate data.
7. The system (100) for evaluation of income and credit score of persons with undocumented income, as claimed in claim 1, wherein the Deep-tech/Neural network algorithm is self-learning and the parameters are updated to precision on every evaluation.
8. A computer implemented method (200) to evaluate the income and credit score comprising of a step of profile creation (201), a step of evaluation (202) of income by analyzing plurality of evaluation parameters , a step of estimation (203) of probability of default , and a step of processing (204) the credit analysis score, Income and Probability of default based on a Deeptech /Neural network algorithm .
9. The computer implemented method (200) to evaluate the income and credit score, as claimed in claim 8, wherein the step of profile creation (201) consists of collecting plurality of data of the customer selected from personal data, financial data, socio-economic data, and medical data, social data, psychometric details.
10. The computer implemented method (200) to evaluate the income and credit score, as claimed in claim 8, wherein the steps of evaluation of income (202), estimation probability of default (203) and processing of credit score (204) is based on Artificial Intelligence/Deep-tech/Neural network based algorithm.
11. A computer implemented method (200) to evaluate the income and credit score, as claimed in claim 8, wherein the evaluation of income, probability of default and credit score is done by the processing unit.
12. A computer implemented method (200) to evaluate the income and credit score, as claimed in claim 8, wherein the plurality of predefined parameters is evaluated based on personal attributes, socio- economic attributes, psychological attributes, and medical data.