Patent ID: 12190375

DETAILED DESCRIPTION

The present invention provides significant technical improvements to facilitate electronic messaging and dynamic data processing. The present invention is directed to more than merely a computer implementation of a routine or conventional activity previously known in the industry as it significantly advances the technical efficiency, access and/or accuracy of communications between devices by implementing a specific new method and system as defined herein. The present invention is a specific advancement in the area of electronic record attribute analysis by providing benefits in data accuracy, data availability and data integrity and such advances are not merely a longstanding commercial practice. The present invention provides improvement beyond a mere generic computer implementation as it involves the processing and conversion of significant amounts of data in a new beneficial manner as well as the interaction of a variety of specialized client and/or third-party systems, networks, and subsystems. For example, in the present invention information may be processed, automatically classified, forecast, and/or predicted via a front-end application server and results may then be analyzed accurately to evaluate the accuracy of various results and/or facilitate predictions associated with future performance, thus improving the overall efficiency of the system associated with message storage requirements and/or bandwidth considerations (e.g., by reducing the number of messages that need to be transmitted via a network). Moreover, embodiments associated with predictive models might further improve risk values, predictions of risk values, allocations of resources, electronic record processing decisions, etc.

In some cases, a risk value associated with an enterprise system may depend at least in part on attribute values of electronic records representing a plurality of potential associations with the enterprise system. For example, the risk value might tend to increase when a specific type of attribute value increases (or decrease when another type of attribute value increases). Moreover, an accurate prediction of the risk value may be desired. Manually entering information to receive predictions and/or decisions about the risk value, however, can be a time consuming and error prone process, especially when a substantial number of electronic records and/or attribute variables may influence the behavior of the system. For example,FIG.1illustrates manual entry of information associated with a risk score. In particular, a display100might include a manual information entry portion110including fields that can be used to manually type in information about a potential association (e.g., an insurance policy number122, location, insured details124, etc.). When the information is entered, the user may select a “Continue” icon130to receive risk information about the potential association. Such an approach can take a substantial amount of time and some of the information might be incorrectly entered.

Also note that an enterprise might, under certain conditions, gather additional information about a potential association to improve the decision-making process. This approach, however, may be burdensome (e.g., because the additional information may be gathered even when it is not necessarily required). Similarly, a large and diverse amount of third-party information might further complicate these tasks. Note that improving the performance of the system and/or the accuracy of decisions made about potential associations might result in substantial improvements to the operation of the enterprise and/or one or more networks associated with the enterprise (e.g., by reducing an overall number of electronic messages that need to be created and transmitted via the network).

It would be desirable to provide systems and methods to automatically create risk scores for electronic records in a way that provides faster, more accurate results and that allows for flexibility and effectiveness when responding to those results.FIG.2is a high-level block diagram of a system200according to some embodiments of the present invention. In particular, the system200includes a front-end application computer server250and automated scoring analysis computer260that may access information in a potential association computer store210(e.g., storing a set of electronic records representing risk associations, each record including, for example, one or more communication addresses, attribute variables, etc.). The automated scoring analysis computer260may also retrieve information from a historical data store220(e.g., storing information about prior insurance claims) to create a scoring model265. The front-end application computer server250and/or automated scoring analysis computer260may also exchange information with a remote administrator computer230(e.g., via a firewall232). According to some embodiments, an interactive graphical user interface platform255of the front-end application computer server250(and, in some cases, third-party data) may facilitate forecasts, decisions, predictions, and/or the display of results via one or more remote computers270(e.g., to gather additional information about a potential association) and/or the remote administrator computer230. For example, the remote administrator computer230may transmit a selection to automated scoring analysis computer260. Based on the selection, the automated scoring analysis computer260may interact with the front-end application computer server250to calculate a risk score that can be returned to the remote administrator computer230(after the front-end application computer server250transmits additional questions and receives responses as appropriate). Note that the front-end application computer server250and/or any of the other devices and methods described herein might be associated with a third party, such as a vendor that performs a service for an enterprise.

The front-end application computer server250and/or the other elements of the system200might be, for example, associated with a Personal Computer (“PC”), laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. According to some embodiments, an “automated” front-end application computer server250(and/or other elements of the system200) may facilitate analysis of electronic records in the potential association data store210. As used herein, the term “automated” may refer to, for example, actions that can be performed with little (or no) intervention by a human.

As used herein, devices, including those associated with the front-end application computer server250and any other device described herein may exchange information via any communication network which may be one or more of a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.

The front-end application computer server250may store information into and/or retrieve information from the data stores210,220. The potential association data store210might, for example, store electronic records representing a plurality of potential associations, each electronic record having a set of attribute values. The potential association data store210may also contain information about prior and current interactions with parties, including those associated with remote communication devices. The potential association data store210may be locally stored or reside remote from the front-end application computer server250. As will be described further below, the potential association data store210may be used by the front-end application computer server250and/or automated scoring analysis computer260in connection with an interactive user interface. Although a single front-end application computer server250is shown inFIG.2, any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the front-end application computer server250and automated scoring analysis computer260might be co-located and/or may comprise a single apparatus.

Note that the system200ofFIG.2is provided only as an example, and embodiments may be associated with additional elements or components. According to some embodiments, the elements of the system200automatically transmit information associated with an interactive user interface display over a distributed communication network.FIG.3illustrates a method300that might be performed by some or all of the elements of the system200described with respect toFIG.2, or any other system, according to some embodiments of the present invention. The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a computer-readable storage medium may store thereon instructions that when executed by a machine result in performance according to any of the embodiments described herein.

At S310, an automated scoring analysis computer may access information in a historical data store, including sets of attribute values and prior results for a plurality of prior associations, to create a scoring model. According to some embodiments, each electronic record is associated with a potential insurance policy and the scoring model is used to calculated risk score associated with an underwriting grade. In such embodiments, the scoring model might be associated with a predictive model. Note that the scoring model might be associated with quantile bands and/or clustering techniques.

Each potential insurance policy might be associated with, for example, an insurance policy quote, an existing insurance policy, or an insurance policy renewal. At least one of the attribute values of a potential insurance policy might include information about an insured associated with the insurance policy, such as an annual sales amount, an industry classification, prior claim information, etc. Moreover, at least one of the attribute values might include information about the insurance policy, such as a property deductible amount, a business personal property limit, a building limit, a building limit per square foot, etc. According to some embodiments, at least one of the attribute values might include information about a property associated with the insurance policy, such as a construction occupancy protection and exposure attribute, a census attribute, a geography attribute, etc. In still other embodiments, at least one of the attribute values may include information about a location associated with the insurance policy, such as a quality index, an earthquake zone, a wind zone, a sub-wind zone, etc.

At S320, an indication may be received of a selected potential association from a potential association data store, including a set of attribute values. For example, an indication of an electronic record of interest might be associated with an insurance policy search input, such as an insurance policy number, a selected location, an insured name, an insurance policy description, a building identifier, etc.

At S330, the system may calculate a risk score for the selected potential association based on the scoring model. According to some embodiments, the risk score may further be calculated based on third-party data, social media data, location information, business credit information, prior interactions, employee sentiment data, building information, etc.

Depending on the risk score calculated at S330, a front-end application computer server may automatically select a workflow path form a set of potential workflow paths. For example, the front-end application server might modify a set of information requests (or questions) to be transmitted from the enterprise in connection with the selected potential association. The automatic modification of the set of information requests might comprise, for example, removing information requests when the risk score is below a pre-determined threshold value. As another example, the system may automatically arrange to complete the potential association based on the calculated risk score and responses to the modified set of information requests. The automatic completion of the potential association might include, for example:completing the potential association when the risk score is below a first pre-determined threshold value;preventing the potential association when the risk score is above a second pre-determined threshold value; andapplying at least one completion rule when the risk score is between the first and second pre-determined threshold values.

Thus, embodiments may provide a streamlined way to process insurance quotes to improve the ease of doing business, allow for expansion in targeted types of business as desired, and/or provide efficient loss cost management. Moreover, a business referral model may quantify the risk of a customer at the first notice of a quote to enable a differentiated quoting process to enhance a customer's experience and improve underwriting quality. The risk score may identify high severity risks to enable triaging of quotes based on the risk level.

FIG.4is an example of an electronic record risk score interactive user display400according to some embodiments. The display400includes a find business area410where an insurance policy number search term may be entered422(e.g., to let a user indicate a property of interest), a location may be selected420, an insured name, class description, and insurance agency424may be entered and/or a building identifier may be selected (e.g., “Building 001”). The display400may further include modifiable coverage information (e.g., building coverage, liability coverage, etc.). The display400also includes a map area440that may provide third-party mapping information (e.g., from a GOOGLE® mapping platform) on a street-level442basis for the property of interest450. A display pointer460might be used in the map area440to dynamically re-center the display, zoom in or out, etc. According to some embodiments, the display further includes an image of insured property.

FIG.5is a high-level block diagram of an insurance underwriting system500according to some embodiments. The system500includes customer data510such as name, address, legal entity, and class (which might be pre-filled by the system but open to modification). The customer data510is sent to a scoring service530via an administrator interface520. The scoring service530may use historical data540, such as prior quotes, premiums, etc. and third-party data550, such as crime data, education data, sales data, population density data, social media information, industry codes, credit information, employee count data, Uniform Commercial Code (“UCC”) information, etc. to calculate a risk score that is returned via the administrator interface520.

According to some embodiments, the scoring service530might incorporate a predictive model532. The predictive model532might use the historical data540to provide insight into which risks are more (or less) likely to have large losses. The predictive model532may quantify the probability of a risk have aggregate losses exceeding a pre-determined threshold and might incorporate, according to some embodiments, logistic regression and training data. The predictive model might also identify high-risk segments that are heavily underwritten (to quantify the probability of a quote being underwritten). The risk score generated by the scoring service530might then be used to suppress follow-up information requests (as being unnecessary for relatively low risk customers) and/or to implement (or prohibit) an automated process. The underwriting model534might incorporate, according to some embodiments, logistic regression and training data (based on quotes by line of business).

FIGS.6and7are examples of risk scores according to some embodiments. In particular,FIG.6illustrates600a display610for a business with a risk score640of “10” (on a scale from 1, representing lowest risk, to 100, representing highest risk). As a result, the business is classified as “low effort” and a first workflow path is selected. Note that a computer pointer620might be used to select elements of the display610to receive additional information about those elements (e.g., how the risk score640was calculated). The display610further includes an image630of the business. Note that the first workflow path may improve yield (allowing for winning more lower risk business) and the streamlined ease of the system may drive more quotes. As another example,FIG.7illustrates700a display710for a business with a risk score740of “70.” As a result, the business is classified as “higher effort” and a second workflow path is selected. For example, the quote might require manual underwriter review.

Note that the display710further includes an image730of the business. According to some embodiments, a risk score is further calculated based on results of an automated image recognition process. For example,FIGS.8and9illustrate the use of image tagging in accordance with some embodiments. In particular, FIG. illustrates800an image that has been automatically tagged as “acceptable” because of exterior building maintenance810, high quality landscaping820, and security lighting or cameras830. In contrast,FIG.9illustrates900an image that has been automatically tagged as “unacceptable” because of barbed wire on the property910, the storage of pallets outside920, and poor building maintenance930.

The embodiments described herein may be implemented using any number of different hardware configurations. For example,FIG.10illustrates an apparatus1000that may be, for example, associated with the systems200,500described with respect toFIGS.2and5, respectively. The apparatus1000comprises a processor1010, such as one or more commercially available Central Processing Units (“CPUs”) in the form of one-chip microprocessors, coupled to a communication device1020configured to communicate via a communication network (not shown inFIG.10). The communication device1020may be used to communicate, for example, with one or more remote administrator computers and or communication devices (e.g., PCs and smartphones). Note that communications exchanged via the communication device1020may utilize security features, such as those between a public internet user and an internal network of the insurance enterprise. The security features might be associated with, for example, web servers, firewalls, and/or PCI infrastructure. The apparatus1000further includes an input device1040(e.g., a mouse and/or keyboard to enter information about properties, mapping data, historic information, predictive models, etc.) and an output device1050(e.g., to output reports regarding underwriting decisions and recommendations).

The processor1010also communicates with a storage device1030. The storage device1030may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device1030stores a program1015and/or a risk evaluation tool or application for controlling the processor1010. The processor1010performs instructions of the program1015, and thereby operates in accordance with any of the embodiments described herein. For example, the processor1010may access information in a historical data store or database1100, including sets of attribute values and prior results for a plurality of prior associations, to create a scoring model. An indication of a selected potential association from a potential association data store1060may be received, including a set of attribute values. The processor1010may then calculate a risk score for the selected potential association based on the scoring model. The processor1010may automatically select a workflow from a plurality of potential workflows (e.g., the processor might modify a set of information requests to be transmitted from the enterprise and/or also automatically arrange to complete the potential association based on the calculated risk score and responses to the modified set of information requests).

The program1015may be stored in a compressed, uncompiled and/or encrypted format. The program1015may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor1010to interface with peripheral devices.

As used herein, information may be “received” by or “transmitted” to, for example: (i) the front-end application computer server1000from another device; or (ii) a software application or module within the front-end application computer server1000from another software application, module, or any other source.

In some embodiments (such as shown inFIG.10), the storage device1030further stores the potential association data store1060, the historical database1100(e.g., associated with a set of destination communication addresses, attribute variables, etc.), an underwriting grade database1200, and a third-party database1080. Examples of databases that might be used in connection with the apparatus1000will now be described in detail with respect toFIGS.11and12. Note that the databases described herein are only examples, and additional and/or different information may be stored therein. Moreover, various databases might be split or combined in accordance with any of the embodiments described herein. For example, the historical database1100and/or underwriting grade database1200might be combined and/or linked to each other within the program1015.

Referring toFIG.11, a table is shown that represents the historical database1100that may be stored at the apparatus1000according to some embodiments. The table may include, for example, entries associated with properties previously evaluated by an underwriter. The table may also define fields1102,1104,1106,1108,1110,1112for each of the entries. The fields1102,1104,1106,1108,1110,1112may, according to some embodiments, specify: an insurance policy identifier1102, a customer identifier1104, an attribute1106, an attribute value1108, a risk score1110, and prior claim information1112. The historical database1100may be created and updated, for example, based on information electrically received from various computer systems, including third-party applications.

The insurance policy identifier1102may be, for example, a unique alphanumeric code identifying an insurance policy that may be reviewed by an underwriter. According to some embodiments, the insurance policy identifier1102might be associated with the insurance policy number search box422described with respect toFIG.4. The customer identifier1104may, according to some embodiments, identify a party seeking an insurance quote. The attribute1106may represent a type of parameter associated with the policy identifier1102(e.g., geographic information, census data, etc.). The attribute value1108may represent the actual value of the attribute1106(e.g., as determined during an insurance policy quote process). Note that although a single attribute1106and associated value1108are shown inFIG.11for each insurance policy, in actuality many different pairs of data might be utilized. The risk score1110might represent, for example, a grade, category, numerical value, rank, etc. indicating an amount of risk that might be associated with the policy identifier1102with respect to applicable attributes1106. The prior claim information1112might indicate a number of insurance claims and/or an overall amount of losses associated with the insurance policy.

Referring toFIG.12, a table is shown that represents the underwriting grade database1200that may be stored at the apparatus1000according to some embodiments. The table may include, for example, entries associated with properties to be evaluated by an underwriter. The table may also define fields1202,1204,1206,1208,1210for each of the entries. The fields1202,1204,1206,1208,1210may, according to some embodiments, specify: an insurance policy identifier1202, a customer identifier1204, an attribute1206, an attribute value1208, and a risk score1210. The underwriting grade database1200might be created and/or updated based on information received from various computer systems, including third-party applications, during an insurance quote process.

The insurance policy identifier1202may be, for example, a unique alphanumeric code identifying an insurance policy that may be reviewed by an underwriter. According to some embodiments, the insurance policy identifier1202might be associated with the insurance policy number search box422described with respect toFIG.4. The customer identifier1204may, according to some embodiments, identify a party seeking an insurance quote. The attribute1206may represent a type of parameter associated with the policy identifier1202(e.g., geographic information, census data, etc.). The attribute value1208may represent the actual value of the attribute1206(e.g., as determined during an insurance policy quote process). The attribute value risk score1210might represent, for example, a grade, category, numerical value, rank, etc. indicating an amount of risk that might be associated with the policy identifier1202with respect to that set of attributes1206. The risk score1210might be used to select an appropriate workflow path, initiate an electronic flag or message, etc.

According to some embodiments, one or more predictive models may be used to predict or forecast future events. Features of some embodiments associated with a predictive model will now be described by first referring toFIG.13.FIG.13is a partially functional block diagram that illustrates aspects of a computer system1300provided in accordance with some embodiments of the invention. For present purposes it will be assumed that the computer system1300is operated by an insurance company (not separately shown) for the purpose of supporting an insurance underwriting process (e.g., to help accurately make decisions regarding insurance risk, coverages, etc.).

The computer system1300includes a data storage module1302. In terms of its hardware the data storage module1302may be conventional, and may be composed, for example, by one or more magnetic hard disk drives. A function performed by the data storage module1302in the computer system1300is to receive, store and provide access to both historical transaction data (reference numeral1304) and current transaction data (reference numeral1306). As described in more detail below, the historical transaction data1304is employed to train a predictive model to provide an output that indicates an identified performance metric and/or an algorithm to risk score performance factors, and the current transaction data1306is thereafter analyzed by the predictive model. Moreover, as time goes by, and results become known from processing current transactions (e.g., underwriting, clustering, and/or attribute grading decisions), at least some of the current transactions may be used to perform further training of the predictive model. Consequently, the predictive model may thereby appropriately adapt itself to changing conditions.

Either the historical transaction data1304or the current transaction data1306might include, according to some embodiments, determinate and indeterminate data. As used herein and in the appended claims, “determinate data” refers to verifiable facts such as the age of a building; a property size; a policy date or other date; a driver age; a time of day; a day of the week; a geographic location, address or ZIP code; and a policy number.

As used herein, “indeterminate data” refers to data or other information that is not in a predetermined format and/or location in a data record or data form. Examples of indeterminate data include narrative speech or text, information in descriptive notes fields and signal characteristics in audible voice data files.

The determinate data may come from one or more determinate data sources1308that are included in the computer system1300and are coupled to the data storage module1302. The determinate data may include “hard” data like an insured or claimant name, type of business, industry classification code, policy number, address, an underwriter decision, etc. One possible source of the determinate data may be the insurance company's insurance policy database (not separately indicated).

The indeterminate data may originate from one or more indeterminate data sources1310, and may be extracted from raw files or the like by one or more indeterminate data capture modules1312. Both the indeterminate data source(s)1310and the indeterminate data capture module(s)1312may be included in the computer system1300and coupled directly or indirectly to the data storage module1302. Examples of the indeterminate data source(s)1310may include data storage facilities for document images, for text files, and digitized recorded voice files. Examples of the indeterminate data capture module(s)1312may include one or more optical character readers, a speech recognition device (i.e., speech-to-text conversion), a computer or computers programmed to perform natural language processing, a computer or computers programmed to identify and extract information from narrative text files, a computer or computers programmed to detect key words in text files, and a computer or computers programmed to detect indeterminate data regarding an individual.

The computer system1300also may include a computer processor1314. The computer processor1314may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor1314may store and retrieve historical insurance transaction data1304and current transaction data1306in and from the data storage module1302. Thus, the computer processor1314may be coupled to the data storage module1302.

The computer system1300may further include a program memory1316that is coupled to the computer processor1314. The program memory1316may include one or more fixed storage devices, such as one or more hard disk drives, and one or more volatile storage devices, such as RAM devices. The program memory1316may be at least partially integrated with the data storage module1302. The program memory1316may store one or more application programs, an operating system, device drivers, etc., all of which may contain program instruction steps for execution by the computer processor1314.

The computer system1300further includes a predictive model component1318. In certain practical embodiments of the computer system1300, the predictive model component1318may effectively be implemented via the computer processor1314, one or more application programs stored in the program memory1316, and computer stored as a result of training operations based on the historical transaction data1304(and possibly also data received from a third party). In some embodiments, data arising from model training may be stored in the data storage module1302, or in a separate computer store (not separately shown). A function of the predictive model component1318may be to determine appropriate underwriting, clustering, and/or attribute grading decisions for one or more potential insurance policies. The predictive model component may be directly or indirectly coupled to the data storage module1302.

The predictive model component1318may operate generally in accordance with conventional principles for predictive models, except, as noted herein, for at least some of the types of data to which the predictive model component is applied. Those who are skilled in the art are generally familiar with programming of predictive models. It is within the abilities of those who are skilled in the art, if guided by the teachings of this disclosure, to program a predictive model to operate as described herein.

Still further, the computer system1300includes a model training component1320. The model training component1320may be coupled to the computer processor1314(directly or indirectly) and may have the function of training the predictive model component1318based on the historical transaction data1304and/or information about potential insureds. (As will be understood from previous discussion, the model training component1320may further train the predictive model component1318as further relevant data becomes available.) The model training component1320may be embodied at least in part by the computer processor1314and one or more application programs stored in the program memory1316. Thus, the training of the predictive model component1318by the model training component1320may occur in accordance with program instructions stored in the program memory1316and executed by the computer processor1314.

In addition, the computer system1300may include an output device1322. The output device1322may be coupled to the computer processor1314. A function of the output device1322may be to provide an output that is indicative of (as determined by the trained predictive model component1318) particular clustering, attribute grade, and/or underwriting decisions, etc. The output may be generated by the computer processor1314in accordance with program instructions stored in the program memory1316and executed by the computer processor1314. More specifically, the output may be generated by the computer processor1314in response to applying the data for the current simulation to the trained predictive model component1318. The output may, for example, be a numerical estimate and/or likelihood within a predetermined range of numbers. In some embodiments, the output device may be implemented by a suitable program or program module executed by the computer processor1314in response to operation of the predictive model component1318.

Still further, the computer system1300may include an electronic record risk scoring model module1324. The electronic record risk scoring model module1324may be implemented in some embodiments by a software module executed by the computer processor1314. The electronic record risk scoring model module1324may have the function of rendering a portion of the display on the output device1322(e.g., an interactive user display including attribute grades, mapping information, geo cohort data, etc.). Thus, the electronic record risk scoring model module1324may be coupled, at least functionally, to the output device1322. In some embodiments, for example, the electronic record risk scoring model module1324may report results and/or predictions by routing, to an underwriter1328via an electronic record risk scoring platform1326, follow-up underwriting questions, and/or insurance decisions generated by the predictive model component1318. In some embodiments, this information may be provided to an underwriter1328who may also be tasked with determining whether or not the results may be improved (e.g., by further adjusting the models).

In some embodiments described herein, a predictive model may use information obtained during an insurance quote process (e.g., describing a property, a type of business, etc.) to assign a risk score to a potential insurance policy. Note, however, that a predictive model may receive other inputs and/or generate other outputs in accordance with embodiments described herein. For example, a predictive model might receive historic claim information (e.g., associated with other insurance policies within a cluster). According to some embodiments, the predictive model might be run using several different alternate sets of input values and generate predication for each of those scenarios.

Thus, embodiments may provide an automated and efficient way to generate attribute analysis risk scores for a potential insurance policy to help an underwriter make better decisions. Embodiments may also address the need for a consistent and objective determination of how a potential insurance policy should be evaluated.

The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.

Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the displays described herein might be implemented as a virtual or augmented reality display and/or the databases described herein may be combined or stored in external systems). Moreover, although embodiments have been described with respect to particular types of insurance policies, embodiments may instead be associated with other types of insurance policies in additional to and/or instead of the policies described herein (e.g., business insurance policies, automobile insurance policies, etc.). Similarly, although a certain number of attribute grades were described in connection some embodiments herein, other numbers of grades might be used instead. Still further, the displays and devices illustrated herein are only provided as examples, and embodiments may be associated with any other types of user interfaces. For example,FIG.14illustrates a handheld tablet computer1600showing a risk score display1410according to some embodiments. The risk score display1410might include user-selectable data that can be selected and/or modified by a user of the handheld computer1400.

FIG.15illustrates an overall process1500in accordance with some embodiments. At S1510, information about a potential insured, property, building, business, etc. may be collected during an insurance quote or renew process. This information might be gathered, for example, via an interview, telephone call, web-based form, etc. At S1520, the system may interact with an underwriter via an electronic record risk display (e.g., associated with an interactive GUI). The system may then automatically select a workflow path from a plurality of potential workflow paths (e.g., by modifying and information exchange or automatically completing an association). Note that the interactions with the customer and/or an underwriter might be transmitted directly to the potential insured or instead be provided via an insurance agent, a sales representative, a customer service manager, etc.

The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims.