Predictive Key Risk Indicator Identification Process Using Quantitative Methods

• US 20140019194A1
• Filed: 07/12/2012
• Published: 01/16/2014
• Est. Priority Date: 07/12/2012
• Status: Abandoned Application

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Anne, Ajay Kumar
• Application Number:
  US13/547,853
• Publication Number:
  US 20140019194A1
• Time in Patent Office: Days
• US Class Current: 705/7.28
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

Methods, computer-readable media, and apparatuses are disclosed for identifying predictive key risk indicators (KRIs) for organizations and/or firms through the application of specific statistical and quantitative methods that are well integrated with qualitative adjustment. An indicator is a variable with the purpose of measuring change in a phenomena or process. A risk indicator is an indicator that estimates the potential for some form of resource degradation using mathematical formulas or models. Organization/enterprise key risk indicators are an essential arsenal in the risk management framework of any firm or organization and may be required by regulatory agencies.

Claims

1. A computer-assisted method comprising:

identifying a set of key risks using a first triangulation process with risk information for an identified risk;

identifying a set of potential risk indicators associated with the identified risks using a second triangulation process;

conducting, by a risk management computer system, quantitative and statistical analysis to identify a set of statistical associations and a set of predictive relationships of the potential risk indicators and the key risks through correlation testing and regression modeling; and

selecting a set of predictive key risk indicators from the set of statistical associations and the set of predictive relationships.

2. The method of claim 1, further comprising:

setting thresholds for the set of predictive key risk indicators; and

verifying coverage for the set of predictive key risk indicators.

3. The method of claim 2, further comprising:

reporting potential gaps in coverage for the set of predictive key risk indicators.

4. The method of claim 1, further comprising:

pre-processing risk data to perform the quantitative and statistical analysis.

5. The method of claim 4, wherein the pre-processing risk data step includes:

processing, by the risk management computer system, of risk data by building metric risk data sets;

performing, by the risk management computer system, data analysis of the metric risk data sets; and

profiling, by the risk management computer system, the metric risk data sets to enable the quantitative and statistical analysis.

6. The method of claim 4, wherein the pre-preprocessing of risk data step includes a Box-Cox power transformation or a set of time-series plots.

7. The method of claim 1, wherein the first triangulation process includes risk information for the identified risk that includes:

historical losses, emerging risks, and qualitative judgment.

8. The method of claim 1, wherein a historical loss heat map is utilized to identify historical losses.

9. The method of claim 1, wherein the second triangulation process includes:

obtaining monitoring metrics for each of the identified risks, using qualitative judgment to validate and narrow down the monitoring metrics and validate and narrow down the risk indicators, and performing selective causal analysis and hypothesis testing.

10. The method of claim 1, wherein the regression modeling includes metric association with loss frequency and metric association with loss severity.

11. The method of claim 1, wherein during the selecting a set of predictive key risk indicators step, a prioritization scheme is applied that includes the following four components:

quantitative aspects, qualitative feedback, exposure to multiple business units, and historical loss exposure.

12. The method of claim 1, further comprising the step of:

monitoring the set of key risk indicators for performance.

13. An apparatus comprising:

at least one memory; and

at least one processor coupled to the at least one memory and configured to perform, based on instructions stored in the at least one memory;

identifying a set of key risks using a first triangulation process with risk information for an identified risk;

identifying risk indicators associated with the identified risks using a second triangulation process;

pre-processing risk data to perform the quantitative and statistical analysis;

conducting, by a risk management computer system, quantitative and statistical analysis to identify a set of statistical associations and a set of predictive relationships of the risk indicators and the key risks through correlation testing and regression modeling;

selecting a set of predictive key risk indicators from the set of statistical associations and the set of predictive relationships;

setting thresholds for the set of predictive key risk indicators; and

verifying coverage for the set of predictive key risk indicators.

14. The apparatus of claim 13, wherein the at least one processor is further configured to perform:

reporting potential gaps in coverage for the set of predictive key risk indicators.

15. The apparatus of claim 13, wherein the pre-processing risk data instruction includes:

processing, by the risk management computer system, of risk data by building metric risk data sets;

performing, by the risk management computer system, data analysis of the metric risk data sets; and

profiling, by the risk management computer system, the metric risk data sets to enable the quantitative and statistical analysis.

16. The apparatus of claim 15, wherein the pre-preprocessing of risk data instruction includes a Box-Cox power transformation or a set of time-series plots.

17. The apparatus of claim 13, wherein the first triangulation process includes risk information for the identified risk that includes:

historical losses, emerging risks, and qualitative judgment, and further wherein the historical losses are identified by a historical loss heat map.

18. The apparatus of claim 13, wherein the second triangulation process includes:

obtaining monitoring metrics for each of the identified risks, using qualitative judgment to validate and narrow down the monitoring metrics and validate and narrow down the risk indicators, and performing selective causal analysis and hypothesis testing.

19. A computer-readable storage medium storing computer-executable instructions that, when executed, cause a processor to perform a method comprising:

identifying a set of key risks using a first triangulation process with risk information for an identified risk, wherein the first triangulation process includes risk information for the identified risk that includes;

historical losses, emerging risks, and qualitative judgment, and further wherein the historical losses are identified by a historical loss heat map;

identifying risk indicators associated with the identified risks using a second triangulation process, wherein the second triangulation process includes;

obtaining monitoring metrics for each of the identified risks, using qualitative judgment to validate and narrow down the monitoring metrics and validate and narrow down the risk indicators, and performing selective causal analysis and hypothesis testing;

conducting, by a risk management computer system, quantitative and statistical analysis to identify a set of statistical associations and a set of predictive relationships of the risk indicators and the key risks through correlation testing and regression modeling; and

selecting a set of predictive key risk indicators from the set of statistical associations and the set of predictive relationships.

20. The computer-readable medium of claim 19, said method further comprising:

setting thresholds for the set of predictive key risk indicators;

verifying coverage for the set of predictive key risk indicators; and

.

reporting potential gaps in coverage for the set of predictive key risk indicators.

21. The computer-readable medium of claim 19, said method further comprising:

pre-processing risk data to perform the quantitative and statistical analysis.

22. The computer-readable medium of claim 21, wherein the pre-processing risk data instruction includes:

processing, by the risk management computer system, of risk data by building metric risk data sets;

performing, by the risk management computer system, data analysis of the metric risk data sets; and

profiling, by the risk management computer system, the metric risk data sets to enable the quantitative and statistical analysis.

23. The computer-readable medium of claim 19, said method further comprising:

monitoring the set of key risk indicators for performance.

24. The computer-readable medium of claim 19, wherein the regression modeling includes metric association with loss frequency and metric association with loss severity.

25. The computer-readable medium of claim 19, wherein during the selecting a set of predictive key risk indicators instruction, a prioritization scheme is applied that includes the following four components:

quantitative aspects, qualitative feedback, exposure to multiple business units, and historical loss exposure.