Systems and Methods of Predicting Vehicle Claim Re-Inspections

• US 20150213556A1
• Filed: 01/30/2014
• Published: 07/30/2015
• Est. Priority Date: 01/30/2014
• Status: Abandoned Application

• Current Assignee: CCC Information Services, Inc.
• Original Assignee: CCC Information Services, Inc.
• Inventors: Haller, John Laurence Jr.
• Application Number:
  US14/168,327
• Publication Number:
  US 20150213556A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Techniques for determining or predicting re-inspection of a vehicle insurance claim are disclosed. The probability of an occurrence of a re-inspection of a claim (e.g., a re-inspection score) is determined by using a predictive re-inspection model generated based on a data analysis of historical claim data from a plurality of sources. The re-inspection score may be determined prior to a repair facility initially reviewing the claim or viewing the damage to the vehicle, e.g., at FNOL. Inputs to the predictive re-inspection model may include a settlement estimate, and optionally one or more other claim attributes that are strongly correlated to re-inspection. Other re-inspection information may be additionally or alternatively predicted by using the predictive re-inspection model. Candidate claims for re-inspection may be identified by ranking re-inspection scores and/or other re-inspection information.

Claims

1. A method of predicting re-inspection for a vehicle insurance claim, the method comprising:

obtaining, at a first computing device, a settlement estimate of a vehicle insurance claim corresponding to a vehicle covered by an insurance policy provided by an insurance carrier, the settlement estimate based on an inspection of the vehicle and including an estimate of a monetary amount to be paid by the insurance carrier to a repair facility for repairing vehicle damage indicated with the settlement estimate;

causing, by the first computing device, the settlement estimate to be input into a predictive re-inspection model to generate a re-inspection score indicative of a probability of an occurrence of a re-inspection of the vehicle insurance claim, the predictive re-inspection model generated based on historical claim data; and

providing, by the first computing device, an indication of the re-inspection score to at least one of a user interface or to a recipient computing device for determining whether or not a re-inspection of the vehicle insurance claim is warranted.

2. The method of claim 1, wherein:

causing the settlement estimate to be input into the predictive re-inspection model comprises causing the settlement estimate to be input into a linear regression model generated from a regression analysis of historical claim data of a plurality of historical vehicle insurance claims to determine a subset of a plurality of claim attributes that are more strongly correlated to re-inspection occurrences than are other attributes of the plurality of claim attributes; and

the historical claim data includes settlement estimates of the plurality of historical vehicle insurance claims, indications of whether or not re-inspections occurred for the plurality of historical vehicle insurance claims, and a plurality of claim attributes of the plurality of historical vehicle insurance claims.

3. The method of claim 2, wherein the historical claim data further includes indications of respective levels of actual repair quality corresponding to the plurality of historical vehicle insurance claims, and wherein the re-inspection score is generated based on a target level of repair quality for the vehicle.

4. The method of claim 1, wherein causing the settlement estimate to be input into the predictive re-inspection model to generate the re-inspection score comprises causing the settlement estimate and at least a portion of a plurality of claim attributes of the vehicle insurance claim to be input into the predictive re-inspection model to generate the re-inspection score.

5. The method of claim 1, wherein the historical claim data includes claim data corresponding to a plurality of insurance carriers.

6. The method of claim 1, wherein:

the predictive re-inspection model is a first model generated based on a first data analysis of the historical claim data; and

obtaining the settlement estimate comprises generating, by the first computing device, the settlement estimate by inputting one or more claim attributes of the vehicle insurance claim into the first model or into another model generated based on another data analysis of the historical claim data to obtain the settlement estimate.

7. The method of claim 1, wherein obtaining the settlement estimate comprises receiving, by the first computing device, the settlement estimate from one of:

a computing device corresponding to the insurance carrier, a computing device corresponding to the repair facility, or a user interface.

8. The method of claim 1, wherein providing the indication of the re-inspection score to the recipient computing device comprises providing the indication of the re-inspection score to a computing device corresponding to the insurance carrier.

9. The method of claim 1, wherein obtaining the settlement estimate comprises obtaining a settlement estimate generated at First Notice of Loss (FNOL).

10. The method of claim 1, further comprising determining, by the first computing device, an amount of a predicted financial profit or loss of a performance of the re-inspection of the vehicle insurance claim.

11. The method of claim 1, wherein at least a portion of the method is performed by a processor of the first computing device executing computer-executable instructions stored on a memory of the first computing device.

12. An apparatus for predicting re-inspection for a vehicle insurance claim, the apparatus comprising:

one or more tangible, non-transitory, computer-readable storage media storing thereon computer-executable instructions for generating a predictive re-inspection model, the computer-executable instructions executable by one or more processors to;

perform a predictive analysis on claim data corresponding to a plurality of historical vehicle insurance claims, the claim data including settlement estimates of the plurality of historical vehicle insurance claims, indications of whether or not re-inspections occurred for the plurality of historical vehicle insurance claims, and a plurality of claim attributes of the plurality of historical vehicle insurance claims; and

determine, based on the predictive analysis, a set of independent variables of the predictive re-inspection model, the set of independent variables comprising a subset of the plurality of claim attributes that are more strongly correlated to occurrences of re-inspections than are other attributes of the plurality of claim attributes;

generate the predictive re-inspection model based on the predictive analysis; and

determine, using the predictive re-inspection model, a re-inspection score for a vehicle insurance claim corresponding to a vehicle covered by an insurance policy provided by an insurance carrier, the re-inspection score based on a settlement estimate of the vehicle insurance claim,

wherein the settlement estimate is based on a previous inspection of the vehicle, and the re-inspection score is indicative of a probability of an occurrence of a re-inspection of the vehicle insurance claim and is for use in determining whether or not a re-inspection of the vehicle is warranted.

13. The apparatus of claim 12, wherein the settlement estimate of the vehicle insurance claim is an input into the predictive re-inspection model.

14. The apparatus of claim 12, further comprising additional computer-executable instructions stored on the one or more tangible, non-transitory, computer-readable storage media and executable by the one or more processors to predict a supplement amount corresponding to additional costs that are predicted to be identified from the re-inspection of the vehicle insurance claim.

15. The apparatus of claim 12, wherein the predicted supplement amount is an output of the predictive re-inspection model or is an output of a predictive supplement model generated based on another predictive analysis of the claim data corresponding to the plurality of historical vehicle claims.

16. The apparatus of claim 12, wherein the claim data further includes indications of respective levels of repair quality corresponding to the plurality of historical vehicle insurance claims, and wherein determining the re-inspection score is further based on a target level of repair quality for the vehicle.

17. The apparatus of claim 12, wherein the claim data corresponds to a plurality of insurance carriers.

18. The apparatus of claim 12, further comprising additional computer-executable instructions stored on the one or more tangible , non-transitory, computer-readable storage media and executable by the one or more processors to obtain the settlement estimate, wherein the settlement estimate is obtained by one of:

receiving the settlement estimate from a computing device corresponding to the insurance carrier;

receiving the settlement estimate from a computing device corresponding to a repair facility;

receiving the settlement estimate from a user interface;

or

generating, by using the predictive re-inspection model generated based on the predictive analysis of the claim data or by using a predictive settlement model generated from another predictive analysis of the claim data, the settlement estimate based on at least a portion of the plurality of claim attributes of the vehicle insurance claim.

19. The apparatus of claim 12, wherein the previous inspection of the vehicle is associated with a First Notice of Loss (FNOL) of the vehicle insurance claim.

20. A method of identifying vehicle insurance claims for re-inspection, the method comprising:

obtaining, at a computing device, a set of re-inspection scores respectively corresponding to a set of vehicle insurance claims, wherein the re-inspection scores are determined by providing settlement estimates of the set of vehicle insurance claims to a predictive re-inspection model, the predictive re-inspection model is generated from a predictive analysis of historical vehicle claim data, and each of the re-inspection scores is indicative of a probability of an occurrence of a re-inspection of a respective vehicle insurance claim;

ranking, by the computing device, members of the set of vehicle insurance claims based on the set of re-inspection scores;

identifying, by the computing device, a subset of the set of vehicle insurance claims for re-inspection based on the rankings and on a re-inspection threshold; and

providing an indication of the subset of the set of vehicle insurance claims to at least one of a user interface or another computing device.

21. The method of claim 20, wherein obtaining the set of re-inspection scores corresponding to the set of vehicle insurance claims comprises obtaining the set of re-inspection scores corresponding to a set of vehicle insurance claims being serviced by a particular repair facility.

22. The method of claim 20, wherein the historical vehicle claim data is obtained from a plurality of insurance carriers.

23. The method of claim 20, wherein ranking the members of set of vehicle insurance claims comprises ranking the members based on respective differences between respective re-inspection scores and respective settlement estimates.

24. The method of claim 20, further comprising adjusting the re-inspection threshold.

25. The method of claim 20, wherein identifying the subset of the set of vehicle insurance claims based on the re-inspection threshold comprises identifying the subset of the set of vehicle insurance claims based on at least one of:

a threshold level or a threshold percentage.

26. The method of claim 20, wherein providing the indication of the subset of the set of vehicle insurance claims to another computing device comprises providing the indication of the subset of the set of vehicle insurance claims to a computing device associated with an insurance carrier.