Method and system for detecting business behavioral patterns related to a business entity

• US 20060015377A1
• Filed: 07/14/2004
• Published: 01/19/2006
• Est. Priority Date: 07/14/2004
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: LaComb, Christina Ann, Senturk, Deniz, Hoogs, Bethany Kniffin
• Application Number:
  US10/890,836
• Publication Number:
  US 20060015377A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method and system for detecting business behavioral patterns related to a business entity is provided. The method comprises determining a model for business behavioral patterns in which the likelihood of a particular business behavioral pattern is associated with the occurrence of a qualitative event and a quantitative metric. The method further comprises extracting a first data set from a first data source and a second data set from a second data source. The first data set represents the occurrence of the qualitative event associated with the business entity. The second data set represents the quantitative metric associated with the business entity. Then a first confidence attribute and a first temporal attribute associated with the qualitative event is determined. Similarly, a second confidence attribute and a second temporal attribute associated with the quantitative metric are determined. Finally, the likelihood of the particular business behavior pattern is evaluated by running the model based on the first data set, the second data set, the first confidence attribute, the first temporal attribute, the second confidence attribute and the second temporal attribute.

Claims

1. A method for detecting business behavioral patterns related to a business entity comprising:

determining a model for business behavioral patterns in which the likelihood of a particular business behavioral pattern is associated with the occurrence of at least one qualitative event and at least one quantitative metric;

extracting a first data set representing the occurrence of the at least one qualitative event associated with the business entity from a first data source;

extracting a second data set representing the at least one quantitative metric associated with the business entity from a second data source;

determining a first confidence attribute and a first temporal attribute associated with the at least one qualitative event;

determining a second confidence attribute and a second temporal attribute associated with the at least one quantitative metric; and

evaluating the likelihood of the particular business behavior pattern by running the model based on the first data set, the second data set, the first confidence attribute, the first temporal attribute, the second confidence attribute and the second temporal attribute.

2. The method of claim 1, wherein the first data source comprises on-line news sources, commercial news sources, business trade and industry publications, news reports, footnotes to financial statements, and qualitative financial data learned in interviews and discussions with the business entity.

3. The method of claim 1, wherein the second data source comprises financial results and internal financial statements related to the business entity, stock exchange reports and quantitative risk scores.

4. The method of claim 1, wherein the particular business behavioral pattern comprises at least one of likelihood of fraud, financial credit or investment risk and good credit or investment prospect associated with the business entity.

5. The method of claim 1, wherein the first data set comprises verbal or narrative pieces of data representative of one or more business and financial occurrences associated with the business entity.

6. The method of claim 1, wherein the second data set comprises numerical data related to the financial health of the business entity.

7. The method of claim 1, wherein determining a first confidence attribute associated comprises determining a reliability value of the first data source.

8. The method of claim 1, wherein determining a second confidence attribute comprises determining a statistical confidence range of the quantitative metrics.

9. The method of claim 1, further comprising deriving one or more temporal relationships between the qualitative event and the quantitative metric from the first temporal attribute and the second temporal attribute.

10. The method of claim 1, wherein the model is a risk assessment model configured to infer business risk information and evaluate the likelihood of the business behavioral pattern related to the business entity from the at least one qualitative event, the at least one quantitative metric, the first temporal attribute, the second temporal attribute, the first confidence attribute and the second confidence attribute.

11. The method of claim 10, wherein the risk assessment model uses a fusion reasoning methodology to infer the business risk information and evaluate the likelihood of the business behavioral pattern.

12. The method of claim 10, wherein the risk assessment model further comprises extracting additional data from the first data source and the second data source to re-evaluate the business risk information and the business behavioral pattern.

13. The method of claim 1, wherein the model comprises a Bayesian belief network configured to infer business risk information and evaluate the likelihood of the business behavioral pattern related to the business entity from the at least one qualitative event, the at least one quantitative metric, the first temporal attribute, the second temporal attribute, the first confidence attribute and the second confidence attribute.

14. A method of detecting business behavioral patterns related to a business entity comprising:

formulating a risk assessment model related to the business entity;

expressing the risk assessment model as a probabilistic network with node elements, wherein the node elements comprise quantitative data and qualitative data;

determining a temporal attribute and a confidence attribute associated with the qualitative data and quantitative data and populating the node elements with the temporal attribute and confidence attribute;

inferring one or more risk probability values for one or more high level node elements comprising the probabilistic network based on the qualitative data and quantitative data in the node elements and the temporal attribute and confidence attribute; and

detecting the business behavioral patterns related to the business entity based on the one or more inferred risk probability values.

15. The method of claim 14, wherein the quantitative data and the qualitative data in the node elements in relation with the confidence attribute and temporal attribute serve as contributing sources of evidence for the high level node elements to infer the one or more risk probability values in the probabilistic network.

16. The method of claim 14, wherein the quantitative data comprise quantitative metrics and the qualitative data comprise qualitative events related to the business entity.

17. The method of claim 14, further comprising deriving one or more temporal relationships between the qualitative data and quantitative data from the temporal attribute.

18. The method of claim 14, wherein determining a confidence attribute associated with the quantitative data comprises determining a statistical confidence range of the quantitative data.

19. The method of claim 14, wherein determining a confidence attribute associated with the qualitative data comprises determining a reliability value of one or more data sources associated with the qualitative data.

20. The method of claim 14, wherein the risk assessment model comprises a fusion reasoning methodology to analyze the node elements comprising the quantitative data and the qualitative data in relation to the temporal attribute and the confidence attribute to infer the one or more risk probability values and the business behavioral patterns related to the business entity.

21. The method of claim 20, wherein the analysis comprises substantiating, explaining or repudiating the one or more inferred risk probability values related to the business entity from the quantitative data, the qualitative data, the temporal attribute and the confidence attribute.

22. The method of claim 20, wherein the fusion reasoning methodology further comprises extracting additional data from the quantitative data and the qualitative data to re-evaluate the one or more inferred risk probability values and the business behavioral patterns.

23. A system for detecting business behavioral patterns related to a business entity comprising:

a data extraction engine configured to extract;

a first data set representing the occurrence of at least one qualitative event associated with the business entity from a first data source; and

a second data set representing at least one quantitative metric associated with the business entity from a second data source; and

a data modeling engine configured to;

determine business behavioral patterns in which the likelihood of a particular business behavioral pattern is associated with the occurrence of the at least one qualitative event and the at least one quantitative metric;

determine a first confidence attribute and a first temporal attribute associated with the at least one qualitative event;

determine a second confidence attribute and a second temporal attribute associated with the at least one quantitative metric; and

evaluate the likelihood of the particular business behavior pattern based on the first data set, the second data set, the first confidence attribute, the first temporal attribute, the second confidence attribute and the second temporal attribute.

24. The system of claim 23, wherein the first data source comprises on-line news sources, commercial news sources, business trade and industry publications, news reports, footnotes to financial statements, and qualitative financial data learned in interviews and discussions with the business entity.

25. The system of claim 23, wherein the second data source comprises financial results and internal financial statements related to the business entity, stock exchange reports and quantitative risk scores.

26. The system of claim 23, wherein the data modeling engine is configured to determine the first confidence attribute based on a reliability value of the first data source.

27. The system of claim 23, wherein the data modeling engine is configured to determine the second confidence attribute based on a statistical confidence range of the quantitative metrics.

28. The system of claim 23, wherein the data modeling engine is further configured to derive one or more temporal relationships between the qualitative events and quantitative metrics from the first temporal attribute and the second temporal attribute.

29. The system of claim 23, wherein the data modeling engine comprises a risk assessment model configured to infer business risk information and evaluate the likelihood of the business behavioral pattern related to the business entity from the at least one qualitative event, the at least one quantitative metric, the first temporal attribute, the second temporal attribute, the first confidence attribute and the second confidence attribute.

30. The system of claim 29, wherein the risk assessment model further comprises extracting additional data from the first data source and the second data source to re-evaluate the business risk information and the business behavioral pattern.