System and method for assuring the integrity of data used to evaluate financial risk or exposure

• US 20050091151A1
• Filed: 11/15/2004
• Published: 04/28/2005
• Est. Priority Date: 08/23/2000
• Status: Abandoned Application

• Current Assignee: Citibank (South Dakota) NA (Citigroup Incorporated)
• Original Assignee: Citibank (South Dakota) NA (Citigroup Incorporated)
• Inventors: Renzetti, Richard, Coleman, Ronald
• Application Number:
  US10/989,046
• Publication Number:
  US 20050091151A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method and system is provided for assuring the integrity of data used to evaluate financial risk or exposure in trading portfolios such as portfolios of derivative contracts by looking for sweeping changes or statistically significant trends suggestive of possible errors. The method and system uses Content Analysis to measure the changes in the information content or entropy of data to detect abnormal changes that may require human intervention. A graphical user interface can also be provided that provides a mechanism for alerting users of possible errors and also gives an indication of the severity of the detected abnormality.

Claims

1. A method for detecting abnormalities in input data to a financial risk management system, the method comprising:

(a) receiving a set of input data to a financial risk management system;

(b) receiving one or more historical values, each historical value representing a previous set of input data;

(c) calculating the likelihood that changes to the set of input data are the result of one or more errors.

2. The method of claim 1, wherein the input data includes data feeds from one or more data processing systems.

3. The method of claim 1, wherein the input data includes data calculated by a financial risk management system.

4. The method of claim 1, further comprising:

(d) displaying a result based on the calculated likelihood that changes to the set of input data are the result of one or more errors.

5. The method of claim 4, wherein displaying a result includes displaying an icon indicative of the degree of likelihood that changes to the set of input data are the result of one or more errors.

6. The method of claim 1, wherein calculating the likelihood that changes to the set of input data are the result of one or more errors comprises:

(i) calculating the information content of the input data; and

(ii) performing a statistical analysis of the calculated information content relative to the one or more historical values to determine the likelihood that changes to the input data are the result of one or more errors.

7. The method of claim 6, wherein calculating the information content of the input data is performed by calculating the Shannon entropy of the input data.

8. The method of claim 6, wherein the statistical analysis is performed using non-parametric resampling statistics.

9. The method of claim 6, wherein the statistical analysis is performed using Bayesian statistics.

10. The method of claim 6, wherein the statistical analysis is performed using parametric statistics.

11-20. -20. (canceled)

21. A system for detecting abnormalities in input data to a financial risk management system, the system comprising:

a means for receiving a set of input data to a financial risk management system;

a means for receiving one or more historical values, each historical value representing a calculated content from a previous set of input data; and

a means for calculating the likelihood that changes to the set of input data are the results of one or more errors.

22. The system of claim 21, further comprising:

a graphical user interface means for displaying a result based on the calculated likelihood that changes to the set of input data are the result of one or more errors.

23. A method for detecting abnormalities in data related to a financial risk management system, the method comprising:

(a) receiving a set of data;

(b) receiving one or more historical values, each historical value representing a previous set of data;

(c) calculating the likelihood that changes to the set of data are the result of one or more errors.

24. The method of claim 23, wherein the set of data includes input data to a financial risk management system.

25. The method of claim 23, wherein the set of data includes data calculated by a financial risk management system.

26. The method of claim 23, wherein each value of the one or more historical values represents the information content of a previous set of data.

27. The method of claim 23, wherein calculating the likelihood that changes to the set of data are the result of one or more errors comprises:

(i) calculating the information content of the data; and

(ii) performing a statistical analysis of the calculated information content relative to the one or more historical values to determine the likelihood that changes to the data are the result of one or more errors.

28. A method to identify potential errors in data input into a financial risk assessment process, the method comprising:

determining a first characteristic of a historical financial risk assessment data set, the first characteristic being a function of at least the entropy of the set;

determining the first characteristic of a current financial risk assessment data set; and

determining a likelihood that the current data set is from the population of the historical data set based at least in part on the first characteristics of the current and historical sets.

29. A method for detecting abnormalities in input data to a financial risk management system, the method comprising:

(a) receiving a set of input data to a financial risk management system implemented on a data processing server;

(b) receiving one or more historical values from a computer storage device, each historical value representing a previous set of input data; and

(c) calculating the likelihood that changes to the set of input data are the result of one or more errors on one or more central processing units coupled to the computer storage device.

30. A method for determining a confidence level for a set of input data to a financial risk management system, the method comprising:

receiving a historical data set having a first characteristic;

receiving a set of input data having a second characteristic; and

determining a confidence level for the set of input data based upon a comparison between the first and second characteristics.