Predictive exposure modeling system and method

• US 20060259333A1
• Filed: 05/16/2005
• Published: 11/16/2006
• Est. Priority Date: 05/16/2005
• Status: Abandoned Application

• Current Assignee: James Ettwein, Philip J. Pyburn, William Snyder
• Original Assignee: Inventum Corporation
• Inventors: Snyder, William, Ettwein, James, Pyburn, Philip J.
• Application Number:
  US11/129,791
• Publication Number:
  US 20060259333A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

An audit selection method (10) for commercial casualty policies (such as workers'"'"' compensation, employer'"'"'s liability, and general liability policies) can include the steps of determining (14) a probability of under-reported exposure for a given policy (12) using classification modeling, identifying (24) a source of under-reported exposure using classification modeling, and selecting an audit protocol (28, 30, 32) effective or most effective in uncovering an under-reported exposure based on the probability determined and the source of under-reported exposure identified. Identifying the source of under-reported exposure can be done by identifying at least one among payroll increases, uninsured subcontractors, and misclassified occupations as examples. Determining the probability can be done by classifying the given policy according to a likelihood that an actual exposure for the given policy exceeds an exposure upon which an estimated premium was based requiring an additional premium for the given policy.

Claims

1. An audit selection method for commercial casualty policies, comprising the steps of:

determining a probability of under-reported exposure for a given policy using classification modeling;

identifying a source of under-reported exposure using classification modeling; and

selecting an audit protocol effective in uncovering an under-reported exposure based on the probability determined and the source of under-reported exposure identified.

2. The audit selection method of claim 1, wherein the step of determining the probability of under-reported exposure using classification modeling comprises the step of classifying the given policy according to a likelihood that an actual exposure for the given policy exceeds an exposure upon which an estimated premium was based requiring an additional premium for the given policy.

3. The audit selection method of claim 2, wherein the commercial casualty policies are worker'"'"'s compensation policies and the actual exposure is based on at least one among an employer'"'"'s payroll and occupational classes assigned to an employer'"'"'s employees.

4. The audit selection method of claim 1, wherein the step of selecting the audit protocol is done by selecting the audit protocol most effective in uncovering the under-reported exposure.

5. The audit selection method of claim 1, wherein the method further comprises the step of auditing the given policy using an audit selected by the audit protocol selected among a physical audit, a telephonic audit, and a mail audit.

6. The audit selection method of claim 5, wherein the method further comprises the step of adjusting the premium of the given policy based on the results of the audit.

7. The audit selection method of claim 1, wherein the classification modeling uses a historical premium audit database containing insured data, policy data, agent data, historical premium audit results, claims data, and econometric data.

8. The audit selection method of claim 7, wherein the classification modeling uses a historical premium audit database containing insured data selected among industry codes, location codes, number of employees by occupational class, age of employees by occupational class, total revenue, historical premium audit results, prior cancellation for non-payment of audit premium, total payroll, ownership structure, number of years in business, previous insurance carrier, and prior year premium;

policy data selected among main occupation class code, secondary occupation class code, estimated premium, experience modifiers, rating elements, policy type;

agent data selected among location, agency type, and agency premium audit history;

historical premium audit results selected among additional payroll by class, payroll attributable to subcontractors, and class additions/modifications;

claims data selected among loss history and cause of reported injuries; and

econometric data selected among industry growth in operation locations, employment growth in operation locations, and industry profitability.

9. The audit selection method of claim 1, wherein the classification modeling uses a historical premium audit database that compiles additional records to the database as additional audits are performed.

10. The audit selection method of claim 1, wherein the method further comprises the step of pre-classifying the given policy if the given policy is among a policy subject to an interim audit or subject to a premium below a predetermined threshold.

11. The audit selection method of claim 1, wherein the method further comprises the step of pre-classifying the given policy if the given policy was subject to an audit in prior years.

12. The audit selection method of claim 1, wherein the step of determining the probability of under-reported exposure using classification modeling comprises the step of using probabilistic structured decision analysis.

13. The audit selection method of claim 1, wherein the step of identifying the source of under-reported exposure comprises identifying at least one among payroll increases, uninsured subcontractors, and misclassified occupations.

14. A predictive exposure modeling system, comprising:

a first classification engine that determines a probability of under-reported exposure for a given policy using classification modeling;

a second classification engine associated with the first classification engine that identifies a source of under-reported exposure using classification modeling and selects an audit protocol effective in uncovering an under-reported exposure based on the probability determined and the source of under-reported exposure identified; and

a historical audit database coupled to the second classification engine, wherein the historical audit database contains data useful in identifying the source of under-reported exposure and selecting the audit protocol.

15. The predictive exposure modeling system of claim 14, wherein the historical premium audit database contains insured data, policy data, agent data, historical premium audit results, claims data, and econometric data.

16. The predictive exposure modeling system of claim 14, wherein the system further comprises a pre-classifier that uses data selected among an interim audit for the given policy, threshold premium data amounts, and audit data from prior years for the given policy.

17. A machine-readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:

determining a probability of under-reported exposure for a given policy using classification modeling;

identifying a source of under-reported exposure using classification modeling; and

selecting an audit protocol effective in uncovering an under-reported exposure based on the probability determined and the source of under-reported exposure identified.

18. The machine readable storage of claim 17, wherein the machine readable storage further comprises another plurality of code sections executable by the machine for causing the machine to classify the given policy according to a likelihood that an actual exposure for the given policy exceeds an exposure upon which an estimated premium was based requiring an additional premium for the given policy.

19. The machine readable storage of claim 17, wherein the machine readable storage further comprises another plurality of code sections executable by the machine for causing the machine to pre-classify the given policy if the given policy is among a policy subject to an interim audit or subject to a premium below a predetermined threshold or Subject to an audit in prior years.

20. A workers'"'"' compensation insurance policy classification method, comprising the steps of:

determining a likelihood that an actual exposure relating to at least one among payroll and occupational classes exceeds an exposure estimated for determining a premium for a given worker'"'"'s compensation insurance policy using classification modeling;

identifying a source of under-reported exposure using classification modeling that uses data from a historical audit database; and

selecting an audit protocol effective in uncovering an under-reported exposure based on the likelihood determined and the source of under-reported exposure identified.