RISK ASSESSMENT/MEASUREMENT SYSTEM AND RISK-BASED DECISION ANALYSIS TOOL

• US 20120150570A1
• Filed: 08/20/2010
• Published: 06/14/2012
• Est. Priority Date: 08/20/2009
• Status: Active Application

• Current Assignee: Stamford Risk Analytics LLC
• Original Assignee: Stamford Risk Analytics LLC
• Inventors: Guharay, Sabyasachi, Tieng, Joseph, Samad-Khan, Ali
• Application Number:
  US13/391,062
• Publication Number:
  US 20120150570A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The present invention relates to a method and system for more accurately and reliably assessing/measuring risk, and is applicable to all areas of risk management including market, credit, operational and business/strategic risk. An additional aspect of the invention contemplates transforming the resulting risk metrics into risk-based economic capital and/or into decision variables, which can be used to make informed risk-based decisions.

Claims

1. A computer implemented system for estimating the risk of loss for a specified time period, the system comprising:

an input module, operable manually to retrieve and/or receive input information relating to a plurality of observed and/or anticipated loss event occurrences, the input information providing a plurality of loss amount thresholds and the frequency of loss event occurrences at the plurality of loss amount thresholds;

an optimization module, operable to generate ALECs based on three or more parameters, the parameters comprising two or more severity parameters from an assumed loss severity distribution and an average loss frequency parameter for the specified time period, the optimization module;

(a) generating one or more initial value sets of the parameters, the initial value sets being provided as pre-determined values and/or provided randomly from a range and/or received manually, the range being pre-determined and/or received manually,

(b) generating an ALEC for each of the value sets of the parameters,

(c) calculating a weighted error test statistic to measure one or more differences at each loss amount threshold, and/or the aggregated differences, between each generated ALEC and the input information,

(d) where one or more of the differences between any one or more of the ALECs and the input information show an improvement in the weighted error statistic greater than a predetermined rate or where steps (b) to (c) have been repeated less than a predetermined number of times, repeating steps (b) to (c) with new value sets of the parameters, the new value sets of the parameters being calculated to attempt to reduce the error test statistic for those ALECs,

(e) determining, from the ALECs not being affected by step (d), the overall best fit ALEC based on the error test statistics calculated in step (c), and

(f) where the differences between the overall best fit ALEC and the input information exceeds a predetermined precision requirement, and where steps (c) to (e) have been carried out less than a predetermined number of times, repeating steps (c) to (e) with a new weighted error test statistic, thereby providing an estimation ALEC;

wherein the estimation ALEC represents one unique combination of the average frequency of loss event occurrences for the specified time period and the parameters of the assumed loss severity distribution that best approximate the input information;

thereby, from the estimation ALEC, the risk of loss may be determined.

2. The computer implemented system of claim 1, wherein the risk of loss for a given level of loss is described in terms of loss frequency information and expressed as the number of expected loss events in for the specified time period, or expressed as the probability of one or more loss events occurring in the specified time period or expressed as the expected time period between expected loss events, the expected time period between expected loss events being expressed as 1-in-N years.

3. The computer implemented system of either claim 1 or 2, wherein the frequency of loss event occurrences is the number of loss event occurrences within an observation period, or the average number of events for the specified time period, or the time period between loss event occurrences expressed as 1-in-N time periods.

4. A computer implemented system of claim 1, wherein the frequency of loss event occurrences is assumed to be Poisson distributed, enabling the determination of the estimated individual loss frequency distribution for the specified time period and the individual loss severity distribution.

5. A computer implemented system of claim 1, wherein severity is assumed to have a normal or lognormal distribution.

6. The computer implemented system of claim 1, wherein the input module is operable to accept hard data, soft data and/or expert opinion.

7. The computer implemented system of claim 1, wherein the optimization module applies a weighted minimum distance analysis routine, thereby exaggerating the test error statistic and placing greater emphasis on the tail portion of the approximated severity distribution.

8. The computer implemented system of claim 7, wherein the weighted minimum distance analysis routine further exaggerates the test error statistic by applying the log value of the aggregated errors.

9. The computer implemented system of either claim 7 or 8, wherein prior to application of the optimization module, loss information collected by the input module is scaled by dividing all losses by the lowest loss threshold, and after application of the optimization module the mean severity parameter is scaled back.

10. The computer implemented system of claim 1, further comprising means for undertaking Monte Carlo based simulation to estimate the aggregated expected loss, and/or the aggregated unexpected loss at a high confidence level.

11. The computer implemented system of claim 10, further comprising means to calculate the aggregated cost of risk and/or risk adjusted profitability and/or economic risk of capital.

12. The computer implemented system of either claim 10 or 11, further comprising means for conducting risk-based decision analysis, the analysis comparing one or more attributes of the estimated ALECs and/or the simulation results derived from the original input information with one or more hypothetical scenarios, and determining the sensitivities of one or more variances in the hypothetical input information and/or parameters and/or other information for the scenarios.

13. The computer implemented system of claim 12, wherein the other information comprises loss amount limits and/or risk tolerance levels and/or cost of capital and/or cost of controls and/or projected benefit/profit and/or cost and coverage of insurance.

14. The computer implemented system of claim 12, wherein the analysis comprises risk-reward analysis and/or risk-control and/or risk-transfer and/or cost/benefit analysis.

15. The computer implemented system of claim 1, wherein the specified time period is one year.

16. A computer implemented method for estimating the risk of loss for a specified time period, the method comprising the steps of:

retrieving and/or receiving manually, input information relating to a plurality of observed and/or anticipated loss event occurrences, the input information providing a plurality of loss amount thresholds and the frequency of loss event occurrences at the plurality of loss amount thresholds;

generating ALECs based on three or more parameters, the parameters comprising two or more severity parameters from an assumed loss severity distribution and an average loss frequency parameter for the specified time period, and optimizing the ALECs by;

(a) generating one or more initial value sets of the parameters, the initial value sets being provided as pre-determined values and/or provided randomly from a range and/or received manually, the range being pre-determined and/or received manually,

(b) generating a ALEC for each of the value sets of the parameters,

(c) calculating a weighted error test statistic to measure one or more differences at each loss amount threshold, and/or the aggregated differences, between each generated ALEC and the input information,

(d) where one or more of the differences between any one or more of the ALECs and the input information show an improvement in the weighted error statistic greater than a predetermined rate or where steps (b) to (c) have been repeated less than a predetermined number of times, repeating steps (b) to (c) with new value sets of the parameters, the new value sets of the parameters being calculated to attempt to reduce the error test statistic for those ALECs,

(e) determining, from the ALECs not being affected by step (d), the overall best fit ALEC based on the error test statistics calculated in step (c), and

(f) where the differences between the overall best fit ALEC and the input information exceeds a predetermined precision requirement, and where steps (c) to (e) have been carried out less than a predetermined number of times, repeating steps (c) to (e) with a new weighted error test statistic, thereby providing an estimation ALEC;

wherein the estimation ALEC represents one unique combination of the average frequency of loss event occurrences for the specified time period and the parameters of the assumed loss severity distribution that best approximate the input information;

thereby, from the estimation ALEC, the risk of loss may be determined.

17. A machine-readable medium having stored thereon data representing sets of instructions which, when executed by a machine, cause the machine to perform operations for estimating the risk of loss for a specified time period, the operations comprising:

retrieving and/or receiving manually, input information relating to a plurality of observed and/or anticipated loss event occurrences, the input information providing a plurality of loss amount thresholds and the frequency of loss event occurrences at the plurality of loss amount thresholds;

generating ALECs based on three or more parameters, the parameters comprising two or more severity parameters from an assumed loss severity distribution and an average loss frequency parameter for the specified time period, and optimizing the ALECs by;

(a) generating one or more initial value sets of the parameters, the initial value sets being provided as pre-determined values and/or provided randomly from a range and/or received manually, the range being pre-determined and/or received manually,

(b) generating an ALEC for each of the value sets of the parameters,

(c) calculating a weighted error test statistic to measure one or more differences at each loss amount threshold, and/or the aggregated differences, between each generated ALEC and the input information,

(d) where one or more of the differences between any one or more of the ALECs and the input information show an improvement in the weighted error statistic greater than a predetermined rate or where steps (b) to (c) have been repeated less than a predetermined number of times, repeating steps (b) to (c) with new value sets of the parameters, the new value sets of the parameters being calculated to attempt to reduce the error test statistic for those ALECs,

(e) determining, from the ALECs not being affected by step (d), the overall best fit ALEC based on the error test statistics calculated in step (c), and

(f) where the differences between the overall best fit ALEC and the input information exceeds a predetermined precision requirement, and where steps (c) to (e) have been carried out less than a predetermined number of times, repeating steps (c) to (e) with a new weighted error test statistic, thereby providing an estimation ALEC;

wherein the estimation ALEC represents one unique combination of the average frequency of loss event occurrences for the specified time period and the parameters of the assumed loss severity distribution that best approximate the input information;

thereby, from the estimation ALEC, the risk of loss may be determined.

18. A computer implemented method for estimating the risk of loss for a specified time period, the method comprising the steps of:

retrieving and/or receiving manually, input information relating to a plurality of observed and/or anticipated loss event occurrences, the input information providing a plurality of loss amount thresholds and the frequency of loss event occurrences at the plurality of loss amount thresholds;

generating one or more ALECs based on three or more parameters, the parameters comprising two or more severity parameters from an assumed loss severity distribution and an average loss frequency parameter for specified time period; and

optimizing the ALECs by choosing an estimation ALEC from the one or more ALECs, wherein the estimation ALEC represents one unique combination of the average frequency of loss event occurrences for the specified time period and the parameters of the assumed loss severity distribution that best approximate the input information;

wherein the risk of loss is determined from the estimation ALEC.