Method and system for estimating insurance loss reserves and confidence intervals using insurance policy and claim level detail predictive modeling

• US 20060136273A1
• Filed: 09/09/2005
• Published: 06/22/2006
• Est. Priority Date: 09/10/2004
• Status: Abandoned Application

• Current Assignee: Deloitte Development LLC
• Original Assignee: Deloitte Development LLC
• Inventors: Wu, Peter, Lommele, Jan, Guszcza, James, Lucker, John, Zizzamia, Frank
• Application Number:
  US11/223,807
• Publication Number:
  US 20060136273A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A computerized system and method for estimating insurance loss reserves and confidence intervals using insurance policy and claim level detail predictive modeling. Predictive models are applied to historical loss, premium and other insurer data, as well as external data, at the level of policy detail to predict ultimate losses and allocated loss adjustment expenses for a group of policies. From the aggregate of such ultimate losses, paid losses to date are subtracted to derive an estimate of loss reserves. Dynamic changes in a group of policies can be detected enabling evaluation of their impact on loss reserves. In addition, confidence intervals around the estimates can be estimated by sampling the policy-by-policy estimates of ultimate losses.

Claims

1. A computerized method for predicting ultimate losses of an insurance policy, comprising the steps of storing policyholder and claim level data including insurer premium and insurer loss data in a data base, identifying at least one external data source of external variables predictive of ultimate losses of said insurance policy, identifying at least one internal data source of internal variables predictive of ultimate losses of said insurance policy, associating said external and internal variables with said policyholder and claim level data, evaluating said associated external and internal variables against said policyholder and claim level data to identify individual ones of said external and internal variables predictive of ultimate losses of said insurance policy, and creating a predictive statistical model based on said individual ones of said external and internal variables.

2. The method of claim 1, further comprising the steps of creating individual records in said data base for individual policyholders and populating each of said records with premium and loss data, policyholder demographic information, policyholder metrics, claim metrics and claim demographic information.

3. The method of claim 2, wherein said step of associating said external and internal variables with said policyholder and claim level data includes associating at least one of said external and said internal variables with said individual records based on a unique key.

4. The method of claim 1, further comprising the step of normalizing said policyholder and claim level data.

5. The method of claim 4, wherein said step of normalizing said policyholder and claim level data is effected using actuarial transformations.

6. The method of claim 5, wherein said actuarial transformations include at least one of premium on-leveling, loss trending, and capping.

7. The method of claim 5, further comprising the steps of calculating a loss ratio by age of development based on said normalized policyholder and claim level data.

8. The method of claim 7, further comprising the steps of calculating frequency and severity measurements of ultimate losses.

9. The method of claim 7, further comprising the steps of defining a subgroup from said policyholder and claim level data and calculating a cumulative loss ratio by age of development for said subgroup.

10. The method of claim 9, further comprising the step of effecting a statistical analysis to identify statistical relationships between said loss ratio by age of development and said external and internal variables.

11. The method of claim 10, wherein said step of effecting a statistical analysis includes using multiple regression models.

12. The method of claim 1, wherein said at least one external data source includes external variables for business-level data and household-level data.

13. The method of claim 1, wherein said step of evaluating said associated external and internal variables against said policyholder and claim level data is effected using a binning statistical technique.

14. The method of claim 1, wherein said step of evaluating said associated external and internal variables against said policyholder and claim level data further includes the step of examining said external and internal variables for cross-correlation against one another and removing at least a portion of repetitive external and internal variables.

15. The method of claim 1, further comprising the step of dividing said data in said database into a training data set, a testing data set, and a validation data set.

16. The method of claim 15, further comprising the step of using said training data set and said test data set to iteratively generate an initial statistical model.

17. The method of claim 16, wherein said step of using said training data set and said test data set to generate an initial statistical model includes effecting at least one of multiple regression, linear modeling, backwards propagation of errors, and multivariate adaptive regression techniques.

18. The method of claim 17, wherein said step of using said testing data set includes iteratively refining said initial statistical model against overfitting.

19. The method of claim 18, further comprising the step of using said validation data set to evaluate the predictiveness of said initial statistical model.

20. The method of claim 19, further comprising the step of calculating an estimated loss ratio using said initial statistical model to yield said predictive statistical model.

21. The method of claim 20, further comprising the step of applying said predictive statistical model to said data in said data base to yield an estimate of ultimate losses.

22. The method of claim 21, further comprising the steps of aggregating estimated ultimate losses and calculating loss reserves.

23. The method of claim 22, further comprising the step of estimating confidence intervals on said estimated ultimate losses and said loss reserves using a bootstrapping simulation technique.

24. A system for predicting ultimate losses of an insurance policy, comprising a data base for storing policyholder and claim level data including insurer premium and insurer loss data, means for processing data from at least one external data source of external variables predictive of ultimate losses of said insurance policy and at least one internal data source of internal variables predictive of ultimate losses of said insurance policy, means for associating said external and internal variables with said policyholder and claim level data, means for evaluating said associated external and internal variables against said policyholder and claim level data to identify individual ones of said external and internal variables predictive of ultimate losses of said insurance policy, and means for generating a predictive statistical model based on said individual ones of said external and internal variables.

25. The system of claim 24, further comprising means for creating individual records in said data base for individual policyholders and means for populating each of said records with premium and loss data, policyholder demographic information, policyholder metrics, claim metrics and claim demographic information.

26. The system of claim 25, wherein said means for associating said external and internal variables with said policyholder and claim level data includes means for associating at least one of said external and internal variables with said individual records based on a unique key.

27. The system of claim 24, further comprising means for normalizing said policyholder and claim level data.

28. The system of claim 27, wherein said means for normalizing said policyholder and claim level data includes means for effecting actuarial transformations.

29. The system of claim 28, wherein said actuarial transformations include at least one of premium on-leveling, loss trending, and capping.

30. The system of claim 28, further comprising means for calculating a loss ratio by age of development based on said normalized policyholder and claim level data.

31. The system of claim 30, further comprising means for calculating frequency and severity measurements of ultimate losses.

32. The system of claim 30, further comprising means for defining a subgroup from said policyholder and claim level data and means for calculating a cumulative loss ratio by age of development for said subgroup.

33. The system of claim 32, further comprising means for effecting a statistical analysis to identify statistical relationships between said loss ratio by age of development and said external and internal variables.

34. The system of claim 33, wherein said means for effecting a statistical analysis includes means for utilizing multiple regression models.

35. The system of claim 24, wherein said at least one external data source includes external variables for business-level data and household-level data.

36. The system of claim 24, wherein said means for evaluating said associated external and internal variables against said policyholder and claim level data includes means for effecting a binning statistical technique.

37. The system of claim 24, further comprising means for dividing said data in said database into a training data set, a testing data set, and a validation data set.

38. The system of claim 37, further comprising means for iteratively generating an initial statistical model using said training data set and said testing data set.

39. The system of claim 38, wherein said means for iteratively generating an initial statistical model using said training data set and said testing data set includes means for effecting at least one of multiple regression, linear modeling, backwards propagation of errors, and multivariate adaptive regression techniques.

40. The system of claim 39, wherein said means for iteratively generating an initial statistical model using said training data set and said testing data set includes means for iteratively refining said initial statistical model against overfitting using said testing data set.

41. The system of claim 40, further comprising means for evaluating the predictiveness of said initial statistical model using said validation data set.

42. The system of claim 41, further comprising means for calculating an estimated loss ratio using said initial statistical model to yield said predictive statistical model.

43. The system of claim 42, further comprising means for applying said predictive statistical model to said data in said data base to yield an estimate of ultimate losses.

44. The system of claim 43, further comprising means for aggregating estimated ultimate losses and calculating loss reserves.

45. The system of claim 44, further comprising means for estimating confidence intervals on said estimated ultimate losses and said loss reserves including means for effecting a bootstrapping simulation technique.