SYSTEM AND METHOD FOR ASSESSING RISK

• US 20110119211A1
• Filed: 01/26/2010
• Published: 05/19/2011
• Est. Priority Date: 11/17/2009
• Status: Active Grant

• Current Assignee: Endera Systems LLC
• Original Assignee: Infozen, Inc. (Mantech International Corporation)
• Inventors: Wu, Yi, Lay, William Michael, Kouyaté, Alain, Pu, Fan Tao
• Granted Patent: US 8,548,934 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing

Abstract

The invention describes systems and methods of assessing risk using a computer. A computer-based system including an enrollment module, a data aggregation module, a risk assessment module, and a memory is provided for assessing risks. The enrollment receives, at a computer, personal information regarding at least one entity. The data aggregation module receives, at the computer, risk information regarding the entity according to the personal information from at least one data source. The risk assessment module converts the risk information to assessment information. The memory stores the personal information, the risk information, and/or the assessment information on the computer.

Claims

1. A computer-based system for assessing risks, the system comprising:

an enrollment module to receive, at a computer, personal information regarding at least one entity;

a data aggregation module to receive, at the computer, risk information regarding the at least one entity according to the personal information from at least one data source;

a risk assessment module to automatically convert the risk information to assessment information; and

a memory to store the personal information, the risk information, and/or the assessment information on the computer.

2. The computer-based system according to claim 1, wherein the risk information comprises:

criminal history, civil history, terrorist watch lists, traffic violations, loan or debt delinquencies, outstanding wants or warrants, academic disciplinary history, and/or immigration status.

3. The computer-based system according to claim 1, wherein the risk information comprises:

infraction information, punishment information, and disposition information that correspond to the personal information of the at least one entity.

4. The computer-based system according to claim 3, wherein the infraction information, punishment information, and disposition information are automatically converted to standardized codes by assigning numerical values including an infraction code that corresponds to the infraction information, a punishment code that corresponds to the punishment information, and a disposition code that corresponds to the disposition information.

5. The computer-based system according to claim 1, further comprising:

an adjudication module to determine a level of risk corresponding to the at least one entity according to the assessment information.

6. The computer-based system according to claim 5, wherein the level of risk corresponding to the at least one entity is determined according to adjudication parameters received by the adjudication module.

7. The computer-based system according to claim 6, wherein the adjudication parameters are received from a user and are input into the computer or received from a client over a network.

8. A method of assessing risks using a computer, the method comprising:

receiving personal information regarding at least one entity at the computer;

gathering risk information regarding the at least one entity according to the personal information from at least one data source;

automatically converting the risk information to assessment information; and

storing the personal information, the risk information, and/or the assessment information in a memory on the computer.

9. The method according to claim 8, wherein the gathering of the risk information includes obtaining infraction information, punishment information, and disposition information that correspond to the personal information of the at least one entity.

10. The method according to claim 9, wherein the automatic conversion of the risk information to assessment information includes converting the infraction information to an infraction code, the punishment information to a punishment code, and the disposition information to a disposition by assigning standardized numerical values to the infraction information, the punishment information, and the disposition information.

11. The method according to claim 8, wherein the risk information comprises:

criminal history, civil history, terrorist watch lists, traffic violations, loan or debt delinquencies, outstanding wants or warrants, academic disciplinary history, and/or immigration status.

12. The method according to claim 8, further comprising:

determining a level of risk corresponding to the at least one entity according to the assessment information.

13. The method according to claim 12, wherein the level of risk corresponding to the at least one entity is determined according to adjudication parameters.

14. The method according to claim 13, further comprising:

receiving the adjudication parameters by inputting the adjudication parameters into the computer or receiving the adjudication parameters from a client over a network.

15. The method according to claim 10, wherein the standardized infraction code is derived from infraction information by pre-processing the infraction information and using a predictive model to classify the infraction information into the infraction code along with a confidence factor for the classification;

wherein the standardized punishment code is derived from punishment information by pre-processing the punishment information and using the predictive model to classify the punishment information into the punishment code along with a confidence factor for the classification; and

wherein the standardized disposition code is derived from disposition information by pre-processing the disposition information and using the predictive model to classify the disposition information into the disposition code along with a confidence factor for the classification.

16. The method according to claim 15, wherein the pre-processing of the infraction information, the punishment information, and/or the disposition information includes:

performing a number of text-string substitutions to normalize the language of the infraction information, the punishment information, and/or the disposition information;

removing unnecessary text from the infraction information, the punishment information, and/or the disposition information; and

creating specific phrases based on the proximity of two or more words.

17. The method according to claim 15, wherein the predictive model is a statistical pattern learning model which is trained to predict classifications by using examples of text already classified.

18. A computer-readable recording medium containing computer-readable codes providing commands for computers to execute a process including:

receiving personal information regarding at least one entity at the computer;

gathering risk information regarding the at least one entity according to the personal information from at least one data source;

automatically converting the risk information to assessment information; and

storing the personal information, the risk information, and the assessment information on the computer.