Systems and methods for using social network data to validate a loan guarantee
Systems and methods for using social network data to validate a loan guarantee are disclosed. An example system may include a social networking input circuit to interpret a loan guarantee parameter; a social network data collection circuit to collect data using a plurality of algorithms that is configured to monitor social network information about an entity involved in a loan in response to the loan guarantee parameter; and a guarantee validation circuit to validate a guarantee for the loan in response to the monitored social network information.
1. A system, comprising:
a set of processors; and
a non-transitory computer-readable medium storing a set of instructions that, when executed, cause the set of processors to:
interpret, by a social networking input instruction, a loan guarantee parameter;
collect data, by a social network data collection instruction, using a plurality of algorithms that is configured to monitor social network information about an entity involved in a loan in response to the loan guarantee parameter;
determine whether a guarantee for the loan is validated, by a guarantee validation instruction, in response to the monitored social network information;
create a training data set to train a robotic process automation neural network, the training data set comprising feedback data indicating outcomes of success comprising at least one of: verification of covenant defaults, yield outcomes, loan performance outcomes, collateral valuation outcomes, default outcomes, closing rate outcomes, interest rate outcomes, or return-on-investment outcomes; and
iteratively self-adjust the determination of whether the guarantee for the loan is validated based on the feedback data of the training data set to configure a data collection and monitoring action based on at least one attribute of the loan.
2. The system of claim 1 , wherein:
the loan guarantee parameter comprises a financial condition of the entity; and
the entity is a guarantor for the loan.
3. The system of claim 2 , wherein the guarantee validation instruction further causes the set of processors to determine the financial condition based on at least one attribute among: a publicly stated valuation of the entity, a property owned by the entity as indicated by public records, a valuation of a property owned by the entity, a bankruptcy condition of the entity, a foreclosure status of the entity, a contractual default status of the entity, a regulatory violation status of the entity, a criminal status of the entity, an export controls status of the entity, an embargo status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a web site rating of the entity, a plurality of customer reviews for a product of the entity, a social network rating of the entity, a plurality of credentials of the entity, a plurality of referrals of the entity, a plurality of testimonials for the entity, a plurality of behaviors of the entity, a location of the entity, a jurisdiction of the entity, or a geolocation of the entity.
4. The system of claim 1 , wherein:
the set of instructions further includes a data collection instruction to cause the set of processors to obtain information about a condition of a collateral for the loan;
the collateral comprises at least one item among: a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, or an item of personal property; and
the guarantee validation instruction further causes the processors to validate the guarantee of the loan in response to the condition of the collateral for the loan.
5. The system of claim 1 , wherein the social networking input instruction further causes the set of processors to enable a workflow by which a human user enters the loan guarantee parameter to establish a social network data collection and monitoring request.
6. The system of claim 1 , wherein the set of instructions further includes a smart contract instruction to cause the set of processors to automatically undertake an action related to the loan in response to the validation of the loan.
7. The system of claim 6 , wherein:
the action related to the loan is in response to the loan guarantee not being validated; and
the action comprises at least one action among: a foreclosure action, a lien administration action, an interest-rate adjustment action, a default initiation action, a substitution of collateral, a calling of the loan, or providing an alert to a second entity involved in the loan.
8. The system of claim 1 , wherein the training data set comprising further include human user interactions with the social network data collection instruction.
9. A method, comprising:
interpreting a loan guarantee parameter;
collecting data using a plurality of algorithms that is configured to monitor social network information about an entity involved in a loan in response to the loan guarantee parameter;
determining whether a guarantee for the loan is validated in response to the monitored social network information;
creating a training data set to train a robotic process automation neural network, the training data set comprising feedback data indicating outcomes of success comprising at least one of: verification of covenant defaults, yield outcomes, loan performance outcomes, collateral valuation outcomes, default outcomes, closing rate outcomes, interest rate outcomes, or return-on-investment outcomes; and
iteratively self-adjusting the determining whether the guarantee for the loan is validated based on the feedback data of the training data set to configure a data collection and monitoring action based on at least one attribute of the loan.
10. The method of claim 9 , further comprising enabling a workflow by which a human user enters the loan guarantee parameter to establish a social network data collection and monitoring request.
11. The method of claim 9 , further comprising automatically undertaking an action related to the loan in response to the validation of the loan.
12. The method of claim 11 , wherein:
the action related to the loan is in response to the loan guarantee not being validated; and
the action comprises at least one action among: a foreclosure action, a lien administration action, an interest-rate adjustment action, a default initiation action, a substitution of collateral, or a calling of the loan.
13. The method of claim 12 , further comprising determining at least one domain to which a plurality of algorithms will apply.
14. The method of claim 11 , wherein:
the action related to the loan is in response to the loan guarantee not being validated; and
the action comprises providing an alert to a second entity involved in the loan.
15. The method of claim 9 , wherein training data set further comprises at least one of outcomes from or human user interactions with a plurality of algorithms.
16. An apparatus, comprising:
at least one processor; and
a non-transitory computer-readable medium storing a set of instructions that, when executed, cause the at least one processor to:
interpret, by a social networking input instruction, a loan guarantee parameter;
collect data, by a social network data collection instruction, using a plurality of algorithms that is configured to monitor social network information about an entity involved in a loan in response to the loan guarantee parameter;
determine whether a guarantee for the loan is validated, by a guarantee validation instruction, in response to the monitored social network information;
create a training data set to train a robotic process automation neural network, the training data set comprising feedback data indicating outcomes of success comprising at least one of: verification of covenant defaults, yield outcomes, loan performance outcomes, collateral valuation outcomes, default outcomes, closing rate outcomes, interest rate outcomes, or return-on-investment outcomes; and
iteratively self-adjust the determination of whether the guarantee for the loan is validated based on the feedback data of the training data set to configure a data collection and monitoring action based on at least one attribute of the loan.
17. The apparatus of claim 16 , wherein:
the set of instructions further includes a data collection instruction to cause the at least one processor to obtain information about a condition of a collateral for the loan;
the collateral comprises at least one item among: a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, or an item of personal property; and
the guarantee validation instruction further causes the at least one processor to validate the guarantee of the loan in response to the condition of the collateral for the loan.
18. The apparatus of claim 16 , wherein:
the loan guarantee parameter comprises a financial condition of a guarantor of the loan; and
the guarantee validation instruction further causes the at least one processor to determine the financial condition of the guarantor of the loan based on at least one attribute among: a publicly stated valuation of an entity, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition of the entity, a foreclosure status of the entity, a contractual default status of the entity, a regulatory violation status of the entity, a criminal status of an entity, an export controls status of the entity, an embargo status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a website rating of the entity, a set of customer reviews for a product of the entity, a social network rating of the entity, a set of credentials of the entity, a set of referrals of the entity, a set of testimonials for the entity, a set of behavior of the entity, a location of the entity, or a geolocation of the entity.
19. The apparatus of claim 16 , wherein the loan comprises at least one loan type among: an auto loan, an inventory loan, a capital equipment loan, a bond for performance, a capital improvement loan, a building loan, a loan backed by an account receivable, an invoice finance arrangement, a factoring arrangement, a pay day loan, a refund anticipation loan, a student loan, a syndicated loan, a title loan, a home loan, a venture debt loan, a loan of intellectual property, a loan of a contractual claim, a working capital loan, a small business loan, a farm loan, a municipal bond, or a subsidized loan.
20. The apparatus of claim 16 , wherein the social networking input instruction further causes the at least one processor to enable a workflow by which a human user enters the loan guarantee parameter to establish a social network data collection and monitoring request.