Abstract:
A method of determining risk includes comparing case data with a collection of profiles, selecting profiles from the collection of profiles, and aggregating the outcomes associated with the selected profiles to determine the risk associated with the case data.

Description:
RELATED APPLICATIONS 
     The present application is based on provisional patent application control No. 60/151,068, entitled “INSURANCE EXCHANGE”, filed Aug. 27, 1999, the contents of which are hereby incorporated by reference. 
    
    
     MICROFICHE REFERENCE 
     Two microfiche appendixes in the form of microfiche are included in this application that contain material which is subject to copyright protection. Appendix A contains 169 frames, and Appendix B contains 193 frames. The copyright owner has no objection to the facsimile reproduction by anyone of the Appendixes, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. 
     BACKGROUND 
     The present invention relates to systems that use rules and profiles to determine a score that can be correlated with a risk that an event will occur, for example automated underwriting systems that determine overall risk of loss, and to the integration of this type of system in sales and marketing, for example of insurance, loans, and credit cards. 
     Risk engines are automated systems or computer programs that return a risk factor or score based on data. In the context of insurance, a risk engine may be an underwriting computer program that will return a risk factor or score that correlates with a probability of loss for the insurer, based on facts about the insured person or property. The objective of underwriting is to ensure that a risk is placed in the proper rate plan/tier and that appropriate credits are given. Three alternative approaches are generally considered when automating an underwriting process: (1) scoring, a mathematical method that applies a numerical score based on specific attributes; (2) profiling, a scenario-based method that compares a collection of attributes to predefined scenarios, where each scenario has an associated score or risk; and (3) building a neural network, a form of artificial intelligence that develops its own model to make underwriting decisions by training. 
     Scoring methods are known, and are excellent predictors of performance. However, because each individual fact is scored independently, the final decision may be difficult to explain to customers, the method may be difficult to develop, and may be imprecise or inconsistent. 
     Neural network methods are also known, and are very precise and consistent. However, because the details of the underwriting decision are developed through training, the final decision is almost impossible to explain to customers. Furthermore, training may be complex and slow, and performance prediction may be unreliable. 
     Profiling methods are known as well, and in contrast to scoring methods and neural network methods, they are very precise and consistent, and excellent predictors of performance. Furthermore, the final decision reached through profiling is easy to explain to customers, since each result corresponds to a specific scenario. 
     Risk engines that use a profiling method to underwrite personal automobile and property insurance, are known. Since customers of personal automobile and property insurance fall into a rather small number of scenarios, a profiling method for underwriting this type of insurance need not be vary sophisticated, and is easy to develop with known profile building computer programs. A need exists, however, for a business owner policy (BOP) underwriting system. A BOP underwriting system needs to be much more sophisticated than personal automobile and property insurance underwriting systems, because a BOP insures business risk, property risk associated with each business location, and the general liability risk of each business location. 
     Risk engines for underwriting have been implemented in a commercial setting to provide very fast underwriting decision. For example, in one implementation, any agent telephones an underwriter, who then uses a risk engine to make a quick underwriting decision for a policy from one insurance provider. In another implementation, specific pre-selected insurance agents (captive agents) are given direct access to a risk engine via a private computer network, to make quick underwriting decisions from one insurance provider. There exists a need, however, for an implementation that will allow any agent, or even customers, direct access to a risk engine, for a quick underwriting decision for a policy from multiple insurance providers. 
     BRIEF SUMMARY 
     In a first aspect, the present invention is a method of determining risk, comprising comparing case data with a collection of profiles; selecting a plurality of profiles from the collection of profiles, where each profile is associated with an outcome; and aggregating the outcomes of the selected profiles to determine the risk associated with the case data. 
     In a second aspect, the present invention is a risk determining system, comprising a general- or special-purpose computer, a profile database connected to the computer, and a risk engine executed by the computer and connected to the profile database. The risk engine determines the underwriting risk of business owner policies. 
     In a third aspect, the present invention is a computer program product, comprising a computer-readable medium, means stored on the medium for comparing case data with a collection of profiles; means stored on the medium for selecting a plurality of profile from the collection of profiles, where each profile is associated with an outcome; and means stored on the medium for aggregating the outcomes of the selected profiles to determine a risk associated with the case data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various other objects, features and attendant advantages of the present invention will be more fully appreciated as the same becomes better understood from the following detailed description when considered in connection with the accompanying drawings in which like reference characters designate like or corresponding parts throughout the several views and wherein: 
         FIG. 1  is a flow chart of an embodiment of a risk engine of the present invention; 
         FIG. 2  illustrates an embodiment of an automated underwriting system of the present invention; 
         FIGS. 3 and 4  illustrates the hierarchy of dimensions, risk components and underwriting variables in a BOP; 
         FIG. 5  illustrates gradients and example gradient definitions; 
         FIGS. 6-8  are decision trees used to determine gradient definitions; 
         FIG. 9  shows examples of tier 1 profiles; 
         FIG. 10  illustrates aggregation of tier 1 profile outcomes; 
         FIGS. 11-13  show a specific example of a BOP risk determination; 
         FIG. 14  illustrates aggregation of tier 1 profile outcomes; and 
         FIG. 15  shows a collection of profiles. 
     
    
    
     DETAILED DESCRIPTION 
     Although the following description and examples of the various aspects of the invention are presented in the context of selling and underwriting insurance, in particular commercial property and casualty lines, the invention is applicable to other business areas, for example the lending of money, credit card issuance, and insurance claim processing, as well as other types of insurance, such medical insurance, commercial automobile insurance, workers compensation insurance, professional liability insurance, umbrella liability insurance, recreational package insurance, and life insurance. 
     To apply a traditional profiling method to create a risk engine for a BOP, profiles associated with each separate part insured, such as the business risk, property risk associated with each business location, and the general liability risk associated with each business location, need to be aggregated together. This results in enormous and complex profiles (superprofile). Furthermore, the number of these superprofile needed becomes so numerous they cannot be handled efficiently. 
     The present invention includes a risk engine that does not aggregate profiles together to create a superprofile, but rather uses separate profiles for each part insured, and then aggregates the risk associated with these separate profiles to determine an overall risk. This results in fewer profiles that are stored and used for comparison, increasing the simplicity and efficiency as compared with traditional profiling. 
     An insurance policy may include several insured parts, or dimensions, each of which may be classified into one or more classes of risk, the classes of risk are associated with one or more components, and the components are associated with one or more underlying underwriting variables.  FIGS. 3 and 4  illustrate this hierarchy for a BOP. The classes of risk or dimensions  220  include business risk and property risk shown in  FIG. 3 ; and different types of general liability risk are shown in  FIG. 4 . Each class of risk or dimension has components or risk components  230  associated with it, for example business risk is associated with type/breadth of business, financial performance, quality of management and prior insurance. Finally, each component or risk component will have associated with it one or more underlying underwriting variables  240 , for example quality of management is associated with number of losses in the past three years, total losses paid in the past three years, years owned, and years of experience of the manager. 
     The underlying underwriting variables are the types of specific information that are used to ultimately arrive at the finally underwriting decision. For example, total losses paid in the past three years (an underlying underwriting variable) for a customer may be $20,000 (a factor or datum). The quality or gradient of each component will depend on the factors corresponding to the underlying underwriting variables associated with each component. The following tables list underlying underwriting variables for different risk components and the possible gradients for the risk components. 
     
       
         
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
           
               
                   
               
             
             
               
                 BUSINESS RISK 
               
             
          
           
               
                   
                 Underlying Underwriting 
                 Possible Gradient for Risk 
               
               
                   
                 Variables 
                 Component 
               
               
                   
                   
               
             
          
           
               
                 Risk Component: Prior Insurance 
               
             
          
           
               
                   
                 Prior Carrier 
                 Excellent 
               
               
                   
                 Type of Prior Insurance 
                 Good 
               
               
                   
                 Cancelled/Nonrenewed 
                 Average 
               
               
                   
                 Prior Carrier Rating 
                 Poor 
               
               
                   
                 Financial Size Category 
               
             
          
           
               
                 Risk Component: Quality of Management 
               
             
          
           
               
                   
                 # Losses in Past 3 Yrs (Prior Carrier) 
                 Excellent 
               
               
                   
                 Total Loses in Past 3 Yrs (Prior 
                 Good 
               
               
                   
                 Carrier) 
               
               
                   
                 Years Ownership 
                 Average 
               
               
                   
                 Years Experience 
                 Fair 
               
               
                   
                   
                 Poor 
               
               
                   
                   
                 Unacceptable 
               
             
          
           
               
                 Risk Component: Type and Breadth of Business 
               
             
          
           
               
                   
                 Group 
                 Superior 
               
               
                   
                 NAICS Code(s) 
                 Excellent 
               
               
                   
                 Legal Entity 
                 Good 
               
               
                   
                 # of Locations 
                 Average 
               
               
                   
                   
                 Fair 
               
               
                   
                   
                 Poor 
               
             
          
           
               
                 Risk Component: Financial Performance 
               
             
          
           
               
                   
                 Legal Entity 
                 Excellent 
               
               
                   
                 Credit Score 
                 Good 
               
               
                   
                 D&amp;B Rating 
                 Average 
               
               
                   
                 Growth Rate 
                 Poor 
               
               
                   
                 Commercial vs. Residential 
                 Unacceptable 
               
               
                   
                 Commercial Umbrella 
               
               
                   
                 Occupancy Type 
               
               
                   
                 # of Mortgages/Buildings Ratio 
               
               
                   
                   
               
             
          
           
               
                 PROPERTY RISK 
               
             
          
           
               
                   
                 Underlying Underwriting 
                 Possible Gradient for Risk 
               
               
                   
                 Variables 
                 Component 
               
               
                   
                   
               
             
          
           
               
                 Risk Component: Security/Protection 
               
             
          
           
               
                   
                 CS Fire Alarm 
                 Highly Prot. 
               
               
                   
                 Sprinklers 
                 Protected 
               
               
                   
                 CS Burglar Alarm 
                 Average 
               
               
                   
                 Other Protection 
                 Minimal 
               
               
                   
                   
                 Unprotected 
               
             
          
           
               
                 Risk Component: External Exposures 
               
             
          
           
               
                   
                 Occupancy Type 
                 Superior 
               
               
                   
                 Building Sq. Footage 
                 Highly Desirable 
               
               
                   
                 % Unoccupied 
                 Desirable 
               
               
                   
                 Other Occupants 
                 Average 
               
               
                   
                 Occupancy of Adj. Exposure 
                 Poor 
               
               
                   
                   
                 Unacceptable 
               
             
          
           
               
                 Risk Component: Quality of Structure 
               
             
          
           
               
                   
                 Construction Type 
                 Highly Desirable 
               
               
                   
                 Protection Class 
                 Desirable 
               
               
                   
                 Coastal Exposure 
                 Average 
               
               
                   
                 BCEGS 
                 Less Desirable 
               
               
                   
                 Building Conversion 
                 Unacceptable 
               
             
          
           
               
                 Risk Component: Age of Structure 
               
             
          
           
               
                   
                 Age of Bldg. 
                 New 
               
               
                   
                 Roof 
                 Like New 
               
               
                   
                 Electrical 
                 Middle Age 
               
               
                   
                 Heating 
                 Old 
               
               
                   
                 Plumbing 
                 Unacceptable 
               
               
                   
                   
               
             
          
           
               
                 GENERAL LIABILITY RISK 
               
             
          
           
               
                   
                 Underlying Underwriting 
                 Possible Gradient for Risk 
               
               
                   
                 Variables 
                 Component 
               
               
                   
                   
               
             
          
           
               
                 Risk Component: Operations &amp; Completed Operations 
               
             
          
           
               
                   
                 Hours of Oper. 
                 Superior 
               
               
                   
                 % Receipts from Offsite Installation, 
                 Good 
               
               
                   
                 Service, or Repair 
               
               
                   
                 % Sub-contracted 
                 Average 
               
               
                   
                   
                 Below Average 
               
               
                   
                   
                 Poor 
               
               
                   
                   
                 Unacceptable 
               
             
          
           
               
                 Risk Component: Product 
               
             
          
           
               
                   
                 Foreign Product Sales 
                 Good 
               
               
                   
                 Used Product Sales 
                 Caution 
               
               
                   
                 Product Assembly/ 
               
               
                   
                 Modification/Repackaging 
               
             
          
           
               
                 Risk Component: Geographic 
               
             
          
           
               
                   
                 State 
                 Good 
               
               
                   
                 Territory 
                 Average 
               
               
                   
                 Group 
                 Fair 
               
               
                   
                   
                 Poor 
               
             
          
           
               
                 Risk Component: Type of Business 
               
             
          
           
               
                   
                 NAICS Code(s) 
                 Segment Values 
               
             
          
           
               
                 Risk Component: Premises 
               
             
          
           
               
                   
                 # of Swimming Pools 
                 Low Traffic 
               
               
                   
                 # of Units 
                 Average Traffic 
               
               
                   
                 Insured Square Footage 
                 High Traffic 
               
               
                   
                 Annual Receipt 
                 Unacceptable 
               
               
                   
                 Onsite/Local Property Manager 
                 Highly Desirable 
               
               
                   
                 Adjacent Exposure 
                 Desirable 
               
               
                   
                 Parking Lot Square Footage 
                 Average 
               
               
                   
                   
                 Less Desirable 
               
               
                   
                   
                 Undesirable 
               
               
                   
                   
               
             
          
         
       
     
     The gradient of a risk component is determined from the factors corresponding to underlying underwriting variables associated with the risk component, using the gradient definition for each possible gradient of a risk  5  component.  FIG. 5  shows the possible gradients  250  for various risk components  230 , and an exemplary gradient definition  260 . 
       FIGS. 6-8  are decision trees used to determine gradient definitions  260  and the corresponding gradients  250 . Factors are used to determine characteristics, the characteristics being branches of the decision trees. For example, in  FIG. 6 , a characteristic would be 3+ losses in the past three years, or $2,000 to $25,000 in total losses in the past three years. A single factor may define one or more characteristics, or more than one factor may be used to determine a single characteristic, with each characteristic being true or false. 
       FIG. 9  shows examples of tier 1 profiles  280  that relate to different dimensions  220 , and the tier 1 profile outcomes  290 , associated with the exemplary profiles. In this figure, the example includes multiple parts in the dimensions of property and generally liability, in this case, multiple buildings and multiple locations, respectively. Each part will have its own tier 1 profile outcome. 
     When multiple parts are present for any of the dimensions, the individual tier 1 profile outcomes  290  are aggregated by dimension to give aggregated tier 1 profile outcomes  300 , as shown in  FIG. 10 . Finally, the aggregated tier 1 profile outcomes, and any tier 1 profile outcomes for dimensions having only a single part, are aggregated together to form tier 2 profile outcomes  310  (not illustrated).  FIGS. 11-13  provide a specific example starting with components  230  and underlying underwriting variable  240 , then the specific factors or data  270  of this example, the corresponding gradient  250 , and the corresponding tier 1 outcomes  290 . In  FIG. 14 , the specific tier 1 profile outcomes  290  are aggregated into tier 2 profile outcomes  310 . No aggregated tier 1 profile outcomes are shown because each dimension has only a single part in this example. Finally,  FIG. 15  shows a collection of profiles  280 , each profile having a specific gradient  250  associated with each risk component  230  and a corresponding tier 1 profile outcome  290 . 
     The description of  FIGS. 3-15  uses functional profile terminology, i.e. the terminology used by underwriters. Once the profiles are defined in this way, they need to be mapped into a structure that can be processed by a profile-based knowledge-based system, i.e. a technical profile structure. The examples used in the following description of technical profile structure chosen from automobile insurance, and can be similarly applied to other areas, for example a BOP. 
     In the technical profile structure, a profile is a segmentation of risk that is used in underwriting to slot risks and fine-tune the rating process. Profiles are assembled through the selection of zero or more of each of the following: characteristics, collective characteristics and collective-collective characteristics. When referring generically to any of the items that can make up a profile (i.e., characteristics, collective characteristics and/or collective-collective characteristics), the term “profile elements” is used. 
     A collective characteristic is characteristics combined into and/or relationships. A collective characteristic contains one or more combinations of characteristics which are “or&#39;ed” together. Each combination can contain one or more characteristic. For example, a collective characteristic might be “good personal stability” and might contain the following combinations: 
     Combination 1: Age over 40; or 
     Combination 2: Age over 30 and married and at the same job &gt;3 years; or 
     Combination 3: Age over 20 and owns home. 
     In this example, the collective characteristic “good personal stability” would be considered true if any of the three scenarios (combinations) are true. The benefit of using collective characteristics is twofold—(1) allowing the underwriters to manipulate characteristics into and/or relationships, and (2) capturing commonly-used groups of characteristics into reusable elements. Combinations of characteristics are not reusable building blocks because if two collective characteristics utilize exactly the same combination, the combination actually has to be built twice, within the context of the appropriate collective characteristic. 
     To summarize, the rules surrounding collective characteristics are as follows: collective characteristics are formed by including one or more combinations of characteristics together; characteristics within the combinations are “and”ed together, except when the characteristics are from the same characteristic category (as described below); and combinations of characteristics are “or”ed together. 
     A collective-collective characteristic is a structure which allows collective characteristics and/or characteristics to be combined together, typically into ‘and’ relationships (an exception to this rule is described below). For example, a collective-collective characteristic for “good stability” might be formed by combining “good personal stability” and “good financial stability”. 
     To summarize, the rules surrounding collective-collective characteristics are as follows: collective-collective characteristics are formed by including one or more combinations of collective characteristics and/or characteristics together; and the collective characteristics and characteristics within a collective-collective characteristic will always be “and”ed together, except when any of the collective characteristics are from the same collective category or when any of the characteristics are from the same characteristic category (as described below). 
     A collective category is somewhat analogous to a functional profile component in that it allows for categorization of collective characteristics and collective-collective characteristics into functional groups. Examples might include driving record, stability, prior insurance, etc. Although the collective category is a mechanism to allow classification, it also affects the definitions of profiles in that any two or more collective characteristics which are from the same collective category and are combined into either a collective-collective characteristic or into a profile will be evaluated together with an ‘or’ relationship. Likewise, any two or more collective-collective characteristics which are from the same collective category and are combined into a profile will be evaluated together with an ‘or’ relationship. Consider an example where the following are combined into a profile: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 Collective Category 
                 Collective-Collective characteristic/ 
               
               
                   
                   
                 Collective Characteristic 
               
               
                   
                 Stability 
                 Good stability Marginal stability 
               
               
                   
                 Driving record 
                 Clean driving record 
               
               
                   
                   
               
             
          
         
       
     
     A profile consisting of the above items would read: “If (Good stability or Marginal stability) AND Clean driving record”. 
     To summarize, the rules surrounding collective categories are as follows: collective characteristics and collective-collective characteristics are placed within collective categories when defined; if any two or more collective characteristics are from the same collective category and are used together to form either a collective-collective characteristic or a profile then the collective characteristics are “or”ed together; and If any two or more collective characteristics and/or collective-collective characteristics are from the same collective category and are used together to form a profile then the collective characteristics and/or the collective-collective characteristics are “or”ed together. 
     Gradients, depending on their complexity, will map to either collective-collective characteristics, collective characteristics or characteristics. 
     The definition of characteristic is consistent between the functional profile and technical profile definitions; characteristics are assessments that look at individual underwriting variables and compare them to thresholds/values. An example of a characteristic is “0 At-Fault Accidents in 3 Years.” 
     A characteristic category allows characteristics that are constructed from the same factors, or data elements to be grouped together. Examples might include age, prior insurance type, vehicle performance type, minor violations, major violations, etc. Characteristic categories also affect the definitions of profiles in that any two or more characteristics which are from the same characteristic category and are combined into either a collective characteristic or collective-collective characteristic or into a profile will be evaluated together with an ‘or’ relationship. For example, if two age characteristics are ‘very young’ (ages 16 to 21) and ‘young’ (ages 22 to 26) and if these two characteristics are combined into a profile, then they would be evaluated as ‘very young’ OR ‘young’. 
     To summarize, the rules surrounding characteristic categories are as follows: a characteristic belongs to a specific characteristic category; and if any two or more characteristics are from the same characteristic category and are used together to form either a collective characteristic or a collective-collective characteristic or a profile then the characteristics are “or”ed together. 
     Additional profiling functionality may be incorporated within a specific profile-based underwriting system. This functionality may be added to address specific issues or requirements, including the ability to create location-specific profiles, to track related profile elements which tie to the same gradient together, the ability to track different “versions” of profiles, the ability to indicate that a gradient must be met by any or all operators and/or any or all vehicles, and the ability to define profiles that ensure particular groups of characteristics are met for the same operator and/or vehicle. 
     Location-specific profiles allow the system to support transactions from multiple states; each state may have unique underwriting requirements. Although states have different underwriting guidelines, most states adhere to “common”, or similar, types of requirements. This system ensures that redundant storage of profiles and profile elements that are actually the same are minimized, but also allows the capability to tie together profiles which are “almost” the same with minor location differences. 
     To this end, characteristics, collective characteristics and collective-collective characteristics are defined independent of location. Each of the items are then placed in a general “pool” which will be available to each state for defining profiles. At the profile level, there is a field to indicate for which location the profile is applicable. This location could be “default” or may contain a specific location code (i.e., state or foreign location). The following are examples (for simplicity, each profile is referred to by a number, rather than a name):
         Profile  101 , which applies to all states. In this case, there would be one profile  101  and its location would be “default”.   Profile  102 , which has a default profile that applies to most states and two location-specific versions that apply to OH and IL, and does not apply to AL. In this case, there would be one profile  102  with a location of “default”, one profile  102  with a location of OH, one profile  102  with a location of IL and one profile  102  with a location of AL. The profile  102  for AL would simply include no elements and, therefore, would never match any transactions.   Profile  103 , which applies only to AL, OH and DE. In this case, there would be one profile  103  with a location of AL, one profile  103  with a location of OH and one profile with a location of DE. Note that in this case there would be no default profile.       

     If the above profiles were the only ones defined in the system and if an insurance application was received from each of the states listed below, the indicated profiles would be the only ones considered for evaluation:
         NE: Profile  101 -Default, Profile  102 -Default   OH: Profile  101 -Default, Profile  102 -OH, Profile  103 -OH   IL: Profile  101 -Default, Profile  102 -IL   AL: Profile  101 -Default, Profile  103 -AL   DE: Profile  101 -Default, Profile  102 -Default, Profile  103 -DE       

     This approach has the advantages of drastically reducing redundant storage of like profiles (Note: there will be duplicate storage if two or more locations override a default profile in exactly the same way); allowing all location versions of the same profile to be tracked together so that global performance can be determined (for example, all preferred company customers with good prior insurance, good. stability and good driving record); and allowing underwriters to tie functionally similar profiles together. If there is no link between like profiles across locations, then it is very difficult for underwriters to sift through profiles and identify the similar ones. This design also facilitates the modification process if a profile needs to be changed and the change needs to be made in all states that use that profile. 
     Using a common pool of profile elements, it is preferable that each element be precisely named to reflect its contents. For example, if there are two collective characteristics for Good Stability—one which includes credit in its definition and one which does not, then they would be named “good stability including credit” and “good stability excluding credit” or something similar. 
     Profile elements (e.g., characteristics, collective characteristics, collective-collective characteristics) are the only technical elements that are necessary to fully define profiles. For use in tracking (i.e., tying functionally related profile elements together) and for ease of navigation for business users, gradients may be stored within the system. Gradients may also be related to one or more profile elements which are known as “variants” of the gradient. For example, if a gradient is “good household driving record” and there are two collective characteristics which are used to indicate good household driving record (depending on state requirements)—“good household driving record considering no-fault accidents” and “good household driving record not considering no-fault accidents”, then the two collective characteristics are variants of the gradient. If a user is building a new profile and wants to include “good household driving record”, they would first choose the gradient and would then be prompted to select from the list of profile elements (in this case, collective characteristics), which are variants within the gradient. A gradient may tie to one variant or more than one variant, depending on the requirements of various locations. For example, if all locations use the same definition of “good stability”, then there would only be one variant tied to the gradient. 
     Benefits to this approach include providing gradients as a useful navigation tool for underwriters to navigate to the “variants” they want to include in their profile; using gradients to tie variants together for tracking purposes (for example, there may be a need to tie all variants of good driving record together on a global basis and study the performance of these policies); and eliminating the need to store location-specific characteristics, collective characteristics, etc., which would add significant complexity to the system. 
     Making changes to any profile or profile element is preferably allowed, from a functional perspective, but has major implications to long-term tracking and audibility. Three different approaches are possible: 
     1. Enabling versioning for all profiles and profile elements 
     2. Not enabling versioning for profiles and profile elements 
     3. A hybrid approach—enabling versioning for profiles, but not profile elements 
     Option 1. Versioning Enabled: In this alternative a “version number” is assigned to each profile and each profile element. When a significant change is desired, the item is terminated and a new “version” of the item is created. Multiple versions may have the same item id (e.g., profile id, characteristic id, etc.) to facilitate tracking. Versions may also have both a “start date” and a “stop date” to indicate what timeframes the item is applicable. 
     Option 2. No Versioning: In this option, neither profiles nor profile elements are versioned. Timeframes may still be associated with profiles, however, if a change is required to a profile or profile element, the item may be terminated (i.e., end date is set to the termination date) and a new item may be created with a different id. Any profiles or profile elements that previously included the terminated element may also be updated. For example, if a characteristic needs to be changed, it must first be terminated, then the new definition created, then all profiles, collective characteristics and collective-collective characteristics that included the original characteristic in their definition must now be updated to include the new characteristic id instead. 
     Option 3. Hybrid: This option combines the first and second approaches by enabling versioning for profiles, but not allowing versioning for the profile elements (e.g., characteristics, collective characteristics and collective-collective characteristics). This alternative still forces one to re-map affected profiles, collective characteristics and collective-collective characteristics if a characteristic needs to be revised. 
     When adding a gradient to a profile, the underwriter may need to specify whether the gradient must be matched for ANY operator or ALL operators and/or for ANY vehicle or ALL vehicles. This functionality is nicknamed “Any/All” and may be displayed with the gradients in a profile. For example, one profile may need to specify “Any car is a sports car”, whereas another profile may need to specify “All cars are sports cars”, depending on the profile&#39;s purpose. 
     A subtle point in the definitions of profiles is that in some cases, multiple characteristics should be met for the same operator (e.g., young, good driving record) or vehicle (e.g., high performance, sports car). This functionality may be accomplished by including these characteristics in either a collective characteristic or a collective-collective characteristic. When multiple characteristics that tie to operators or vehicles are included in a combination within a collective characteristic, the characteristics may be matched for the SAME operator or vehicle in order to consider the combination to be true (or met). Likewise, when multiple collective characteristics and/or characteristics which tie to operators or vehicles are included in a collective-collective characteristic, the collective characteristics and/or characteristics must be matched for the SAME operator or vehicle before the collective-collective characteristic is valid. For example, in the collective characteristic “experienced named insured”, the two characteristics—named insured and experienced driver—must be met for the same operator in order for the collective to be met. If the same two characteristics were included directly into the profile, then if ANY operator were the primary operator and ANY operator were experienced, then they would be considered matched. 
     Another common issue is determining whether to evaluate profile elements if the elements are not part of the profile set being evaluated. For example, a “pool” of common characteristics etc. may or may not be in the scope of profiles at a point in time. Evaluating all profile elements allows the tracking of trends over time (and determining if some characteristics should be added to existing profiles), however there are performance implications when unused profile elements are evaluated. Optionally, there all characteristics may be evaluated, but only the collective characteristics and collective-collective characteristics for the profiles within scope. 
       FIG. 1  is a flow chart of an embodiment of a risk engine of the present invention for BOP underwriting. The data of a specific case is first loaded  10 . This data may have been collected by an agent in-person from a customer, and entered into the system via the internet or a private network, or alternatively the customer may enter this data direct via the internet, based on specific question posed from a website. Profile data is then loaded  20 , from a profile database. Next, the components of the profile are matched  30  by first matching the data with characteristics  32 , then matching the data and characteristics with collectives  34 , and finally matching the data, characteristics and collectives with collective-collectives  36 . 
     After the profile components are matched, the components are matched with profiles associated with each class of risk  40 , in this example business risk, property risk, and general liability risk (tier 1 profiles). The components are match with profiles by matching the business risk profiles  42 , matching the property risk profiles  44  and matching the general liability risk profiles  46 . The different tier 1 profiles may be matched in any order, or even simultaneously. 
     Once the tier 1 profiles are determined, an outcome for each profile is determined  50 , based on the specific profile matches. Next, the tier 1 profile outcomes for each class of risk are aggregated  60 , to give an aggregated outcome for each class of risk, in this example an outcome for business risk, an outcome for property risk, and an outcome for general liability risk. The aggregated outcomes are aggregated together  70  to a single risk value or score (tier 2 outcome). The tier 2 outcome is then used to make the underwriting decision, such as the price of the requested insurance policy or if any policy should be issued. 
     An embodiment of the computer code for a risk engine of the present invention is included in Appendix A. 
       FIG. 2  illustrates an embodiment of an automated underwriting system. The risk engine  100  accepts customer data or factors via a network  170  from a user interface  150 , illustrated here as a desktop computer. The risk engine accepts profiles from a profile database  110  used to make the underwriting decision, and sends the results of the underwriting decision to a results database  140  and/or back to the user via the network  170 . Also illustrated is a profile builder  120  connected to the profile database via network  210 , and data analysis reports  130 , connected to the results database via network  190 . The profile builder and the data analysis reports are connected via network  200 . Finally, an underwriter  160  may have access to the results database via a network  180 . 
     The profile builder creates and modifies profiles. Information obtained from the data analysis reports may be used to aid in the construction and modification of profiles. Network  210  connecting profile builder and the data analysis reports is optional, since a person may view the reports and use this knowledge to modify and build profiles. An embodiment of a profile builder is included in Appendix B. 
     The profile database stores the profiles for use by the risk engine. The profile database may be simply an electronic storage medium directly connected to the computer or server on which the risk engine is operating, for example a hard disk drive. Alternatively, the profile database my be remote from the risk engine, and connected to the risk engine via a network such as the internet. 
     Likewise, the results database stores the final results generated by the risk engine. The results database may be simply an electronic storage medium directly connected to the computer or server on which the risk engine is operating, for example a hard disk drive. Alternatively, the results database my be remote from the risk engine, and connected to the risk engine via a network such as the internet. 
     Data analysis reports may provide specific information useful for analyzing the risk engine results. The reports may be stored on an electronic storage medium, such as a hard disk drive, on a computer or server. These reports may include: case details, such as the actual values for specific underwriting fields as well as the components and profiles that match for a particular risk; gradient summaries, such as the number of items and percent of items that match each gradient outcome by component; profile details, such as the number of items and percent of items that match each profile; profile summaries, such as the number of items and percent of items that match each profile outcome; and management tracking, such as results for key metrics on underwriter reviews, overrides, actionable review, close rates, and average written premium. Optionally, these reports may be accessed via a network  200 , such as the internet, by the profile builder or a user of the profile builder. 
     User interface  150  is illustrated as a desktop computer in  FIG. 2 , but may be a server connected to the internet or any network, allowing data to be transferred to and from the risk engine. Underwriter  160  may have access to the results database via a network  180 , or through a telephone, or a private network through a desktop computer. 
     A network may be closed communications lines connecting the illustrated components, or may be a plurality of computers that communicate with each other by means of a wide-area network communications implementation, such as the internet. The network may optionally include servers. 
     Variations of the system shown in  FIG. 2  are possible. For example, there may be multiple profile databases, optionally by different companies, allowing the risk engine to determine final underwriting decisions from each profile database. Likewise, multiple results databases are also possible. Furthermore, there may be multiple user interfaces, connected to one or more risk engines. In a preferred embodiment, the user interface is connected with one or more risk engines via the internet, allowing agents or customers to directly enter data and receive underwriting decisions. The risk engines may use scoring, neural networking, or profiling, preferably profiling, and most preferably profiling with aggregation of tier 1 profile outcomes to give tier 2 profile outcomes. 
     Obviously, numerous modifications and variations of the present invention are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.