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US 11,443,372 B2Granted
Systems and methods for using social network data to validate a loan guarantee
AssigneeStrong Force TX Portfolio 2018, LLC
InventorCharles Howard Cella
Life of this patent
Estimating the term…
Abstract
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.
Claims20
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.
7 more elements
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.
4 more elements
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.
7 more elements
Dependencies are inferred from the claim text.