System for generating historical hail evaluation data

According to some embodiments, a hail history storage device may store information periodically received from a remote third-party weather reporting service. A hail history score request associated with a geographic location and a date range may be received from a remote requester device. Responsive to the received hail history score request, a computer processor of a hail history server may automatically access information in the hail history storage device based on the geographic location and date range. Moreover, the computer processor may automatically evaluate accessed information to calculate a hail history score value. The computer processor may then transmit, to the remote requester device, historical hail evaluation data including the calculated hail history score value.

FIELD

The present invention relates to computer systems and, more particularly, to computer systems associated with hail history evaluation data.

BACKGROUND

An insurer may provide insurance to a business or homeowner to protect against possible damage caused by hail storms. For example, hail may damage a roof, windows, etc. and the risks associated with these types of damages might be covered by an insurance policy. In some cases, the insurer may want to determine whether a new insurance policy is associated with a property that was relatively likely to have previously sustained substantial hail damage. That is, if the insurer determines that prior damage to a roof is very likely to exist (e.g., because of a hail storm that occurred last year), then an inspection of the roof might be warranted before the new insurance policy is issued. Similarly, an insurer might want to verify that a particular property actually experienced a hail storm on a particular date in connection with an insurance claim that is being evaluated.

Hail storms, however, can be a very localized phenomena. One property might sustain substantial hail damage while another nearby property might not experience any damage at all. Moreover, different sizes of hail can cause different types and amounts of damages. It can be difficult, therefore to properly assess the likelihood of hail damage for a particular property. It would therefore be desirable to provide systems and methods to facilitate generation of historical hail evaluation data in an automated, efficient, and accurate manner.

SUMMARY

According to some embodiments, systems, methods, apparatus, computer program code and means may facilitate generation of historical hail evaluation data. In some embodiments, a hail history score request associated with a geographic location and a date range may be received from a remote requester device. Responsive to the received hail history score request, a computer processor of a hail history server may automatically access information in a hail history storage device based on the geographic location and the date range, the hail history storage device storing information periodically received from a remote third-party weather reporting service. Moreover, the computer processor of the hail history server may automatically evaluate the accessed information to calculate a hail history score value. The computer processor of the hail history server may then transmit, to the remote requester device, historical hail evaluation data including the calculated hail history score value.

Some embodiments provide: means for receiving, from a remote requester device, a hail history score request associated with: (i) a geographic location, and (ii) a date range; responsive to the received hail history score request, means for automatically accessing, by a computer processor of a hail history server, information in a hail history storage device based on the geographic location and the date range, the hail history storage device storing information periodically received from a remote third-party weather reporting service; means for automatically evaluating, by the computer processor of the hail history server, the accessed information to calculate a hail history score value; and means for transmitting, from the computer processor of the hail history server to the remote requester device, historical hail evaluation data including the calculated hail history score value.

A technical effect of some embodiments of the invention is an improved and computerized method to facilitate generation of historical hail evaluation data. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.

DETAILED DESCRIPTION

An insurer may provide insurance to a business or homeowner to protect against possible damage caused by hail storms. In some cases, the insurer may want to determine whether a new insurance policy is associated with a property that was relatively likely to have previously sustained substantial hail damage. Similarly, an insurer might want to verify that a particular property actually experienced a hail storm on a particular date in connection with an insurance claim that is being evaluated.

Hail storms, however, can be an extremely localized phenomena. Moreover, different sizes of hail can cause different types and amounts of damages. It can be difficult, therefore to properly assess the likelihood of hail damage for a particular property. It would therefore be desirable to provide systems and methods to facilitate generation of historical hail evaluation data in an automated, efficient, and accurate manner.FIG. 1Ais block diagram of a system100according to some embodiments of the present invention. In particular, the system100includes a hail history server150that may access a third party weather service110. The third party weather service110may periodically transmit (e.g., on a daily basis) information about hail storms to hail history server150at A. The hail history server150may, for example, store this information in a hail history database112. The information about hail storms might be gathered, for example, by a sensor114such as a set of radar antennas located throughout the United States.

The hail history server150may receive a request at B from a requestor device160. For example, an underwriter or claim handler might submit the request to the hail history server150. According to some embodiments, information about received requests may be stored at a hail history request database152. Responsive to the request, the hail history server150might access information in the hail history database112(e.g., associated with prior hail storms). The hail history server150may then evaluate the accessed information and transmit historical hail evaluation data, including a risk score, to the requestor device160at C.

The hail history server150and/or requestor device160might be, for example, associated with a Personal Computer (PC), laptop computer, an enterprise server, a server farm, and/or a database or similar storage devices. The hail history server150may, according to some embodiments, be associated with an insurance provider.

According to some embodiments, an “automated” hail history server150may facilitate the provision of hail history reports including at least one risk score. For example, the hail history server150may automatically generate and email an email indicating that a particular property is “highly” likely to have recently experienced substantial hail damage. 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 hail history server150and 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 hail history server150may store information into and/or retrieve information from the hail history request database152. The hail history request database152might be associated with, for example, an insurance company, an underwriter, or a claim analysis and might also store data associated with past and current insurance claims. The hail history request database152may be locally stored or reside remote from the hail history server150. As will be described further below, the hail history request database152may be used by the hail history server150to generate and/or calculate risk scores. According to some embodiments, the hail history server150communicates information about risk scores to an automated system160, such as by transmitting an electronic file to an underwriter device, an insurance agent or analyst platform, an email server, a workflow management system, a predictive model, etc.

Although a single hail history server150is shown inFIG. 1A, 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 hail history server150and hail history request database152might be co-located and/or may comprise a single apparatus.

Note that the system100ofFIG. 1Ais provided only as an example, and embodiments may be associated with additional elements or components. For example,FIG. 1Bis a block diagram of an insurance system105according to some embodiments described herein. As before, the system106includes the hail history server150that may periodically receive hail information from the third party weather service110(e.g., on a daily basis). The hail history server150may, for example, receive information collected by the sensor114and store the information in a hail history database112.

The hail history server150may interact with an insurance processing system165. The insurance processing system165may, for example, handle underwriting decisions and/or process insurance claims received via a claim system175. The insurance processing system165may interact with remote devices185, such as devices185associated with an insurance claim handler, agent, or underwriter. Any of these devices185and/or the insurance processing system165itself might request historical hail evaluation information from the hail history server150. According to some embodiments, information about received requests may be stored in the hail history request database152. Response to the request, the hail history server150might access information in the hail history database112(e.g., associated with prior hail storms). The hail history server150may then evaluate that accessed information and transmit historical hail evaluation data, including a fraud flag, a risk score, and/or report data to the insurance processing system165.

The insurance processing system165might use the historical hail evaluation data in connection with, for example, claim segmentation logic, insurance handler load balancing and assignment, an anti-fraud wizard (e.g., to tell an underwriter that a particular roof should be inspected from hail damage before issuing an insurance policy for that property), an underwriting process, a risk transfer tool, and/or a property salvage tool. According to some embodiments, the insurance processing system165may use the historical hail evaluation data to interact with external applications195, such as a payment platform, underwriting engine, etc.FIG. 2illustrates a method that might be performed by some or all of the elements of the systems100,105described with respect toFIGS. 1A and 1B, 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 S210, a hail history score request may be received from a remote requester device. The hail history score request may be associated with, for example, a particular geographic location (e.g., a postal address, a latitude and longitude, and/or a personal or business name) and/or a date range (e.g., between a date a roof was repaired and an end date). Note that the hail history score request may further be associated with a potential insurance policy (e.g., a new policy the insurer is considering), a renewal of an existing insurance policy, a commercial insurance policy, a homeowner's insurance policy, and/or an insurance claim (e.g., submitted in connection with an existing insurance policy).

Responsive to the received hail history score request, the hail history server may automatically access information in a hail history storage device at S220based on the geographic location and date range. The hail history storage device may, for example, store data received from a remote third-party weather reporting service. At S230, the hail history server may automatically evaluate the accessed information to calculate a hail history score value (e.g., a numeric value or a risk category classification). For example, a number of risk points may be assigned to each of a plurality of hail sizes. In this case, the information from the remote third-party weather reporting service may be evaluated to determine a total number of risk points associated with the time period.

At S240, the hail history server may transmit to the remote requester device historical hail evaluation data (e.g., in the form of a hail history score report) including the calculated hail history score value. According to some embodiments, the historical hail evaluation data may also include lightning information, wind gust information, a fraud indication, underwriting information, and/or claim probability information. Note that that information about the historical hail evaluation data might also be transmitted to an email server, a workflow application, a report generator, a social media server, a calendar application, and/or a predictive model.

FIG. 3illustrates a hail history report request user interface300in accordance with some embodiments. In particular, the user interface300includes request details (e.g., a requestor identifier, a request date and/or time, and/or a request identifier). The user interface300may also include hail location criteria310, such as a street address, latitude and longitude information, and/or an electronic file (e.g., containing information about one or more properties). The user interface300may further include information about a date range, such as a period between a start date (or start association, such as when a home was built) and an end date (or end association such as an insurance policy or claim date). According to some embodiments, the hail history score request is further associated with a hail size range320(e.g., the requestor might indicate that he or she is only interested in hail events that had hail with a diameter of two or more inches).

FIG. 4illustrates a hail history report user interface400according to some embodiments. As before, the user interface400includes request details (e.g., a requestor identifier, a request date and/or time, and/or a request identifier), hail location criteria (e.g., a street address, latitude and longitude information, and/or an electronic file), information about a date range, and a hail size range (e.g., a minimum hail size). The hail history report user interface400further includes a results portion410displaying a prior hail damage risk score, one or more hail occurrence dates, and a hail size associated with each hail storm. An insurer might, for example, inspect all roofs for potential insurance policies when the risk score is 3 or higher.

Note that the user interfaces300,400are provided only as examples and actual embodiments may display more and/or different information. For example,FIG. 5illustrates a hail history report request user interface500in accordance with another embodiment. In this case, a map area510may be used to define an area associated with the hail history request (e.g., by using a touchscreen to move a location icon520around the map area510). As other example, a hail history request and/or report might include a roof type, a roof pitch, a building type, and/or solar panel information (e.g., all of which might be used in a risk score calculation).

Thus, embodiments described herein may help identify the potential for hail damage at a particular location based on an address or latitude/longitude point. According to some embodiments, access to such a tool may be limited to employees of an insurance entity who have access to intranet network. The tool might be used, for example, to help determine whether a property's roof should be inspected prior to binding coverage to avoid claims from preexisting damage. The tool might also validate reported dates of loss when claims are submitted. In some cases, the tool might not be suitable for determining the risk of future hail while writing business due to the potential for high hazard areas not being subject to hail in recent years (or low hazard areas having anomalous large hail a number of times in a short timeframe). According to other embodiments, the tool may facilitate the prediction of future hail risk using algorithms and/or predictive models.

The hail sizes may be determined by a third party service that uses a radar-based algorithm to calculate an estimated hail size. The answers from the tool may provide an indication of estimated hail size and/or damage based on assumptions about the radar data. Occasionally, there may be false negatives and positives, so final decisions may be made about a property using additional verifications. Note that the tool may depend on one or more calculations that are based on the number of occurrences and/or the size of hail at a particular location. If these calculations are modified for better performance, a Hail Damage Score Version may be incremented to reflect this change. If the algorithm for the historical hail data from a third party weather reporting service is updated, a Hail Data Version number may be updated. Either of these changes could result in hail history results being different than previously reported.

Some embodiments described herein may leverage internal enterprise geocoder when a postal address is used. There may be times when the geocoder will not place the address directly at the building and could give an answer that is not reflective of the true risk at the structure. Moreover, if a different geocoder is used somewhere in the process it may not give the same answer. Note that if the latitude/longitude is known from another source it may be used as an input to receive an answer with respect to an exact location.

Note that requests might be run on an ad hoc basis or batches might be processed (e.g., for 10,000 records). Large batches might be, for example, run after business hours since they may take several hours to process and could hold up other submitted requests. Batch requests may be, for example, processed one at a time in sequential order.

A Prior Hail Damage Scoring methodology might be, for example, developed using an insurer's claim statistics from historical hail events. Based off of an analysis in claim frequency, it may be determined, for example, that historically 3 inch hail corresponds to an approximate claim rate of X %, 2 inch hail corresponds to Y %, and 1 inch hail corresponds to a Z % claim frequency. Using these as the input to a scoring, an insurer might determine a preliminary scoring methodology to determine a score between 0 and 5.

A scoring methodology may convert the percentage chance of a claim per hail event to a point value and sum the points at a location to determine the risk of a claim and assigns a score as follows:1 inch Hail—X points2 inch Hail—Y points3+ inch Hail—Z points
An overall score might be interpreted as follows: Score 0 (No Hail events recorded between start and end date); Score 1 (Cumulative points between a and b for preexisting hail damage based on historical claims data); Score 2 (Cumulative points between b and c for preexisting hail damage based on historical claims data); Score 3 (Cumulative points between c and d for preexisting hail damage based on historical claims data); Score 4 (Cumulative points between d and e for preexisting hail damage based on historical claims data); and Score 5 (Cumulative points e or greater for preexisting hail damage based on historical claims data). Thus, 3 occurrence of 1 inch hail, 1 occurrence of 2 inch hail and 1 occurrence of 3 inch could result in the calculation of similar (or not similar) risk scores.

The embodiments described herein may be implemented using any number of different hardware configurations. For example,FIG. 6illustrates a hail history platform600that may be, for example, associated with the system100ofFIG. 1A. The hail history platform600comprises a processor610, such as one or more commercially available Central Processing Units (CPUs) in the form of one-chip microprocessors, coupled to a communication device620configured to communicate via a communication network (not shown inFIG. 6). The communication device620may be used to communicate, for example, with one or more remote requestor devices and/or weather reporting services. The hail history platform600further includes an input device640(e.g., a mouse and/or keyboard to enter information about hail risk scoring algorithms) and an output device650(e.g., to output reports regarding hail risk and/or insurance policies).

The processor610also communicates with a storage device630. The storage device630may 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 device630stores a program612and/or a hail history engine or application614for controlling the processor610. The processor610performs instructions of the programs612,614, and thereby operates in accordance with any of the embodiments described herein. For example, the processor610may receive a hail history score request associated with a geographic location and a date range from a remote requester device. Responsive to the received hail history score request, the processor610may automatically access information in a hail history database660(e.g., storing information received from a remote third-party weather reporting service). Moreover, the processor610may automatically evaluate the accessed information to calculate a hail history score value. The processor610may then transmit, to the remote requester device, historical hail evaluation data including the calculated hail history score value.

The programs612,614may be stored in a compressed, uncompiled and/or encrypted format. The programs612,614may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor610to interface with peripheral devices.

As used herein, information may be “received” by or “transmitted” to, for example: (i) the hail history platform600from another device; or (ii) a software application or module within the hail history platform600from another software application, module, or any other source.

In some embodiments (such as shown inFIG. 6), the storage device630includes the hail history database660(e.g., associated with past hail storms, claims, damages, etc.), a hail history request database700(e.g., indicating report requests that have been received and/or fulfilled) and an insurance policy database670. An example of a database that may be used in connection with the hail history platform600will now be described in detail with respect toFIG. 7. Note that the database described herein is only one example, 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 hail history database660and/or hail history request database700might be combined and/or linked to each other within the hail history engine614.

Referring toFIG. 7, a table is shown that represents the hail history request database700that may be stored at the hail history platform600according to some embodiments. The table may include, for example, entries identifying requests for hail history reports. The table may also define fields702,704,706,708,710for each of the entries. The fields702,704,706,708,710may, according to some embodiments, specify: a hail history request identifier702, an insurance policy704, a date range706, a geographic location708, and a calculated score710. The insurance policy database700may be created and updated, for example, based on information electrically received requestor devices and/or third party weather service reporting devices.

The hail history request identifier702may be, for example, a unique alphanumeric code identifying a request for a hail history report or score in connection with an insurance policy704during a specific date range706. The geographic location708might comprise, for example, a street address, latitude/longitude or a name associated with a property. The calculated score710may comprise a numeric value or a risk classification associated with the historical hail evaluation data.

According to some embodiments, one or more predictive models may analyze historic weather and/or claim data to generate underwriting decisions, fraud detection programs, etc. Features of some embodiments associated with a predictive model will now be described by first referring toFIG. 8.FIG. 8is a partially functional block diagram that illustrates aspects of a computer system800provided in accordance with some embodiments of the invention. For present purposes it will be assumed that the computer system800is operated by an insurance company (not separately shown) for the purpose of routing hail history information to underwriters, workflows, and/or claim handlers as appropriate.

The computer system800includes a data storage module802. In terms of its hardware the data storage module802may be conventional, and may be composed, for example, by one or more magnetic hard disk drives. A function performed by the data storage module802in the computer system800is to receive, store and provide access to both historical claim transaction data (reference numeral804) and current claim transaction data (reference numeral806). As described in more detail below, the historical claim transaction data804is employed to train a predictive model to provide an output that indicates hail history data, and the current claim transaction data806is thereafter analyzed by the predictive model. Moreover, as time goes by, and results become known from processing current claim transactions, at least some of the current claim transactions may be used to perform further training of the predictive model. Consequently, the predictive model may thereby adapt itself to changing hail data patterns.

Either the historical claim transaction data804or the current claim transaction data806might 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 date of birth, age or name of a claimant or name of another individual or of a business or other entity; a type of injury, accident, sickness, or pregnancy status; a medical diagnosis; a date of loss, or date of report of claim, or policy date or other date; a time of day; a day of the week; a vehicle identification number, a geographic location; 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. Indeterminate data extracted from medical notes or accident reports might be associated with, for example, an amount of loss and/or details about how an accident occurred.

The determinate data may come from one or more determinate data sources808that are included in the computer system800and are coupled to the data storage module802. The determinate data may include “hard” data like a claimant's name, date of birth, social security number, policy number, address; the date of loss; the date the claim was reported, etc. One possible source of the determinate data may be the insurance company's policy database (not separately indicated). Another possible source of determinate data may be from data entry by the insurance company's claims intake administrative personnel.

The indeterminate data may originate from one or more indeterminate data sources810, and may be extracted from raw files or the like by one or more indeterminate data capture modules812. Both the indeterminate data source(s)810and the indeterminate data capture module(s)812may be included in the computer system800and coupled directly or indirectly to the data storage module802. Examples of the indeterminate data source(s)810may include data storage facilities for document images, for text files (e.g., claim handlers' notes) and digitized recorded voice files (e.g., claimants' oral statements, witness interviews, claim handlers' oral notes, etc.). Examples of the indeterminate data capture module(s)812may 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. For example, claim handlers' opinions may be extracted from their narrative text file notes.

The computer system800also may include a computer processor814. The computer processor814may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor814may store and retrieve historical claim transaction data804and current claim transaction data806in and from the data storage module802. Thus the computer processor814may be coupled to the data storage module802.

The computer system800may further include a program memory816that is coupled to the computer processor814. The program memory816may 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 memory816may be at least partially integrated with the data storage module802. The program memory816may 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 processor814.

The computer system800further includes a predictive model component818. In certain practical embodiments of the computer system800, the predictive model component818may effectively be implemented via the computer processor814, one or more application programs stored in the program memory816, and data stored as a result of training operations based on the historical claim transaction data804(and possibly also data received from a third party weather reporting service). In some embodiments, data arising from model training may be stored in the data storage module802, or in a separate data store (not separately shown). A function of the predictive model component818may be to determine appropriate fraud detection logic and/or underwriting processes. The predictive model component may be directly or indirectly coupled to the data storage module802.

The predictive model component818may 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 system800includes a model training component820. The model training component820may be coupled to the computer processor814(directly or indirectly) and may have the function of training the predictive model component818based on the historical claim transaction data804and/or hail history information. (As will be understood from previous discussion, the model training component820may further train the predictive model component818as further relevant data becomes available.) The model training component820may be embodied at least in part by the computer processor814and one or more application programs stored in the program memory816. Thus the training of the predictive model component818by the model training component820may occur in accordance with program instructions stored in the program memory816and executed by the computer processor814.

In addition, the computer system800may include an output device822. The output device822may be coupled to the computer processor814. A function of the output device822may be to provide an output that is indicative of (as determined by the trained predictive model component818) particular fraud detection information and/or underwriting recommendations. The output may be generated by the computer processor814in accordance with program instructions stored in the program memory816and executed by the computer processor814. More specifically, the output may be generated by the computer processor814in response to applying the data for the current claim transaction to the trained predictive model component818. The output may, for example, be a true/false flag or a number 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 processor814in response to operation of the predictive model component818.

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 hail history parameters might be implemented as dashboard displays and/or the databases described herein may be combined or stored in external systems).

Moreover, the displays300,400,500illustrated with respect toFIGS. 3 through 5are only provided as examples, and embodiments may be associated with any other types of user interfaces. For example,FIG. 9illustrates a handheld hail history user display900according to some embodiments. In this particular user display900, hail history request criteria910and a minimum hail size920may be input by a user (e.g., via a touchscreen interface).

Note that the present invention provides significant technical improvements to facilitate hail history score reports. 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 hail history score reports by implementing a specific new method and system as defined herein. The present invention is a specific advancement in the areas of underwriting decisions and/or claim handling by providing technical 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 insurance, client and/or third party weather systems, networks and subsystems. For example, in the present invention underwriting decisions and/or newly submitted claims may be analyzed and accurately and automatically facilitated.