Abstract:
A computer system includes a data storage module which receives, stores, and provides access to determinate data, raw indeterminate data, and extracted indeterminate data captured by an indeterminate data capture module. The computer system also includes a computer processor, a model training component, and a screening module. The model training component generates the predictive model based upon historical determinate and indeterminate data, and continuously adapts the predictive model with new historical data. The screening module categorizes current claims according to whether they are suitable for predictive analysis by the predictive model. The predictive model is applied to the current claims suitable for predictive analysis to determine a value for each claim indicative of whether the current claim transaction is questionable. The system also includes an output device which outputs the determined value for each claim, and a routing modules which routes claim workflow based on the outputted values.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation application of co-pending U.S. patent application Ser. No. 12/099,388, filed Apr. 8, 2008, the entire contents of which are herein incorporated by reference for all purposes. 
     
    
     FIELD OF INVENTION 
       [0002]    The present invention relates to computer systems and more particularly to computer systems that apply predictive models. 
       BACKGROUND 
       [0003]    U.S. Published Patent Application No. 2005/0276401, which names Madill, Jr., et al. as inventors, discloses a computer system that is pertinent prior art to the present invention. Among other functions, Madill&#39;s computer system may run software that appraises the likelihood that an insurance claim is fraudulent. One aspect of Madill&#39;s software encompasses a predictive model that compares a current claim with a fraud model generated from historical data that reflects past claims that were found to be fraudulent. In the one specific type of fraud model described in Madill&#39;s application, fraud patterns are detected among claimants, witnesses, medical providers, attorneys, repair facilities, etc. 
         [0004]    The present inventors have recognized that the utility of a predictive model, for determining that certain insurance claims should be identified as potentially questionable, and referred for special investigation, can be significantly enhanced by expanding the types of information used to train the predictive model and included in current claim transaction data to which the predictive model is applied. 
       SUMMARY 
       [0005]    A computer system is disclosed which includes a module which captures indeterminate data. The computer system also includes a data storage module in communication with the indeterminate data capture module. Functions performed by the data storage module include receiving, storing and providing access to indeterminate data captured by the indeterminate data capture module. In addition, the data storage module also stores and provides access to determinate data. The determinate and indeterminate data stored by the data storage module is related to historical claim transactions and to current claim transactions. 
         [0006]    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; 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. 
         [0007]    As used herein and in the appended claims, “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, but excludes verifiable facts embedded in narrative text or the like. Examples of indeterminate data include narrative speech or text, information in descriptive notes fields and signal characteristics in audible voice data files. 
         [0008]    The computer system further includes a predictive model component that is coupled to the data storage module and determines whether to identify a given one of the current claim transactions for referral to an investigation unit. 
         [0009]    The computer system also includes a computer processor that executes programmed instructions and stores and retrieves the data related to current claim transactions. 
         [0010]    Further included in the computer system is a program memory, coupled to the computer processor, and which stores program instruction steps for execution by the computer processor. 
         [0011]    A model training component is also included in the computer system. The model training component is coupled to the computer processor and trains the predictive model component based on the data related to the historical claim transactions in accordance with program instructions stored in the program memory and executed by the computer processor. As a consequence, a trained predictive model component is provided. 
         [0012]    Still further included in the computer system is an output device. The output device is coupled to the computer processor and outputs an output indicative of whether the current claim transaction in question should be referred to an investigation unit. The computer processor generates the output in accordance with program instructions in the program memory and executed by the computer processor. The output is generated in response to application of data for the current claim transaction in question to the trained predictive model component. 
         [0013]    The computer system further includes a routing module which directs workflow based on the output from the output device. 
         [0014]    The present inventors have observed that effective indicators in an insurance claim file that a claim may be questionable may be present in indeterminate data such as claim handler&#39;s notes, interview notes, and the like. By incorporating this data, or information extracted therefrom, in historical data used to train a predictive model, and in current transaction information applied to the predictive model, the performance of the predictive model may be enhanced, and the ability of the model to recommend investigation may be improved. As a result, questionable claims may be better identified for evaluation, with an increase in an insurance company&#39;s overall ability to identify issues for further investigation. 
         [0015]    With these and other advantages and features of the invention that will become hereinafter apparent, the invention may be more clearly understood by reference to the following detailed description of the invention, the appended claims, and the drawings attached hereto. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0016]      FIG. 1  is a partially functional block diagram that illustrates aspects of a computer system provided in accordance with some embodiments of the invention. 
           [0017]      FIG. 2  is a block diagram that illustrates a computer that may form all or part of the system of  FIG. 1 . 
           [0018]      FIG. 3  is a block diagram that provides another representation of aspects of the system of  FIG. 1 . 
           [0019]      FIGS. 4  is a flow chart that illustrates a process that may be performed in the computer system of  FIGS. 1-3 . 
           [0020]      FIG. 5  is a flow chart that illustrates aspects of the process of  FIG. 4 . 
           [0021]      FIG. 6  is a flow chart that illustrates aspects of the process of  FIG. 4 . 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    In general, and for the purposes of introducing concepts of embodiments of the present invention, a computer system incorporates a predictive model. The predictive model is trained with historical claim transaction data, and is applied to current claim transactions to determine whether the current claim transactions should be referred to an investigation unit. Both the historical claim transaction data and data representing the current claim transactions include indeterminate data or information extracted therefrom. For example, such data/information may come from narrative text or recorded voice files. 
         [0023]    Features of some embodiments of the present invention will now be described by first referring to  FIG. 1 .  FIG. 1  is a partially functional block diagram that illustrates aspects of a computer system  100  provided in accordance with some embodiments of the invention. For present purposes it will be assumed that the computer system  100  is operated by an insurance company (not separately shown) for the purpose of referring questionable claims to an investigation unit. The claims may be from one or more different lines of insurance, such as worker&#39;s compensation, property and casualty, etc. 
         [0024]    The computer system  100  includes a data storage module  102 . In terms of its hardware the data storage module  102  may be conventional, and may be composed, for example, by one or more magnetic hard disk drives. A function performed by the data storage module  102  in the computer system  100  is to receive, store and provide access to both historical claim transaction data (reference numeral  104 ) and current claim transaction data (reference numeral  106 ). As described in more detail below, the historical claim transaction data  104  is employed to train a predictive model to provide an output that indicates whether a claim should be referred to an investigation unit, and the current claim transaction data  106  is thereafter analyzed by the predictive model. Moreover, as time goes by, and results become known from investigations of 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 patterns of questionable claims. 
         [0025]    Both the historical claim transaction data  104  and the current claim transaction data  106  include determinate and indeterminate data. The determinate data may come from one or more determinate data sources  108  that are included in the computer system  100  and are coupled to the data storage module  102 . The determinate data may include “hard” data like the claimant&#39;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&#39;s policy database (not separately indicated). Another possible source of determinate data may be from data entry by the insurance company&#39;s claims intake administrative personnel. 
         [0026]    The indeterminate data may originate from one or more indeterminate data sources  110 , and may be extracted from raw files or the like by one or more indeterminate data capture modules  112 . Both the indeterminate data source(s)  110  and the indeterminate data capture module(s)  112  may be included in the computer system  100  and coupled directly or indirectly to the data storage module  102 . Examples of the indeterminate data source(s)  110  may include data storage facilities for document images, for text files (e.g., claim handlers&#39; notes) and digitized recorded voice files (e.g., claimants&#39; oral statements, witness interviews, claim handlers&#39; oral notes, etc.). Examples of the indeterminate data capture module(s)  112  may 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 from, e.g., a recording of the individual&#39;s voice or the individual&#39;s body language. 
         [0027]    The present inventors believe that applying a predictive model to both “hard” and “soft” claim information may result in more effective performance by the predictive model than consideration of “hard” information alone. For example, claim handlers&#39; opinions may be extracted from their narrative text file notes. Certain types of claim handlers&#39; information, taken with certain types of hard facts, may be particularly likely to provide a reliable indication that a claim should be referred to an investigation unit. In this way, machine intelligence represented by a predictive model may be melded with the claim handlers&#39; human judgment to produce efficient recognition of questionable claim transactions. This may result in referrals of claim transactions for investigations in cases where the claim handlers&#39; human judgment alone would not result in a referral. The predictive model may determine, for example, that when certain expressions of claim handlers&#39; conclusions coexist with certain “hard” data in a claim transaction file, the claim is particularly likely to be questionable. 
         [0028]    The computer system  100  also may include a computer processor  114 . The computer processor  114  may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor  114  may store and retrieve historical claim transaction data  104  and current claim transaction data  106  in and from the data storage module  102 . Thus the computer processor  114  may be coupled to the data storage module  102 . 
         [0029]    The computer system  100  may further include a program memory  116  that is coupled to the computer processor  114 . The program memory  116  may 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 (random access memory). The program memory  116  may be at least partially integrated with the data storage module  102 . The program memory  116  may 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 processor  114 . 
         [0030]    The computer system  100  further includes a predictive model component  118 . In certain practical embodiments of the computer system  100 , the predictive model component  118  may effectively be implemented via the computer processor  114 , one or more application programs stored in the program memory  116 , and data stored as a result of training operations based on the historical claim transaction data  104  (and possibly also data resulting from training with current claims that have been investigated and found to be proper or improper). In some embodiments, data arising from model training may be stored in the data storage module  102 , or in a separate data store (not separately shown). A function of the predictive model component  118  may be to identify current claim transactions that should be referred to an investigation unit. The predictive model component may be directly or indirectly coupled to the data storage module  102 . 
         [0031]    The predictive model component  118  may 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. 
         [0032]    Still further, the computer system  100  includes a model training component  120 . The model training component  120  may be coupled to the computer processor  114  (directly or indirectly) and may have the function of training the predictive model component  118  based on the historical claim transaction data  104 . (As will be understood from previous discussion, the model training component  120  may further train the predictive model component  118  as further relevant claim transaction data becomes available.) The model training component  120  may be embodied at least in part by the computer processor  114  and one or more application programs stored in the program memory  116 . Thus the training of the predictive model component  118  by the model training component  120  may occur in accordance with program instructions stored in the program memory  116  and executed by the computer processor  114 . 
         [0033]    In addition, the computer system  100  may include an output device  122 . The output device  122  may be coupled to the computer processor  114 . A function of the output device  122  may be to provide an output that is indicative of whether (as determined by the trained predictive model component  118 ) a particular one of the current claim transactions should be referred to an investigation unit. The output may be generated by the computer processor  114  in accordance with program instructions stored in the program memory  116  and executed by the computer processor  114 . More specifically, the output may be generated by the computer processor  114  in response to applying the data for the current claim transaction to the trained predictive model component  118 . The output may, for example, be 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 processor  114  in response to operation of the predictive model component  118 . 
         [0034]    Still further, the computer system  100  may include a routing module  124 . The routing module  124  may be implemented in some embodiments by a software module executed by the computer processor  114 . The routing module  124  may have the function of directing workflow based on the output from the output device. Thus the routing module  124  may be coupled, at least functionally, to the output device  122 . In some embodiments, for example, the routing module may direct workflow by referring, to an investigation unit  126 , current claim transactions analyzed by the predictive model component  118  and found to be questionable. In particular, the questionable current claim transactions may be referred to investigative analysts  128  who are employed in the investigation unit  126 . The investigation unit  126  may be a part of the insurance company that operates the computer system  100 , and the investigative analysts may be employees of the insurance company. 
         [0035]      FIG. 2  is a block diagram that illustrates a computer  201  that may form all or part of the system  100  of  FIG. 1 . 
         [0036]    As depicted, the computer  201  includes a computer processor  200  operatively coupled to a communication device  202 , a storage device  204 , an indeterminate data capture device  206 , one or more other input devices  207  and an output device  208 . Communication device  202  may be used to facilitate communication with, for example, other devices (such as personal computers—not shown in  FIG. 2 —assigned to individual employees of the insurance company). The indeterminate data capture device  206  may include any one or more of the devices referred to above in connection with block  112  in  FIG. 1  and may have the functions ascribed above to those devices. The other input device(s)  207  may comprise, for example, a keyboard, a keypad, a mouse or other pointing device, a microphone, knob or a switch, an infra-red (IR) port, a docking station, and/or a touch screen. The other input device(s)  207  may be used, for example, to enter information. Output device  208  may comprise, for example, a display (e.g., a display screen) a speaker, and/or a printer. 
         [0037]    Storage device  204  may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., magnetic tape and hard disk drives), optical storage devices, and/or semiconductor memory devices such as Random Access Memory (RAM) devices and Read Only Memory (ROM) devices. 
         [0038]    In some embodiments, the hardware aspects of the computer  201  may be entirely conventional. 
         [0039]    Storage device  204  stores one or more programs or portions of programs (at least some of which being indicated by blocks  210 - 214 ) for controlling processor  200 . Processor  200  performs instructions of the programs, and thereby operates in accordance with the present invention. In some embodiments, the programs may include a program or program module  210  that acts as a predictive model for determining whether to refer current claim transactions to an investigation unit. The training function for the predictive model  210  is not indicated separately in  FIG. 2  from the predictive model itself. 
         [0040]    Another program or program module stored on the storage device  204  is indicated at block  212  and is operative to allow the computer  201  to route or refer current claim transactions to insurance company employees as appropriate based on the results obtained by applying the predictive model  210  to the data which represents the current claim transactions. 
         [0041]    Still another program or program module stored on the storage device  204  is indicated at block  214  and engages in database management and like functions related to data stored on the storage device  204 . There may also be stored in the storage device  204  other software, such as one or more conventional operating systems, device drivers, communications software, etc. The historical claim transaction data  104  and the current claim transaction data  106 , as previously described with reference to  FIG. 1 , are also shown in  FIG. 2  as being stored on the storage device  204 . 
         [0042]      FIG. 3  is another block diagram that presents the computer system  100  in a somewhat more expansive or comprehensive fashion (and/or in a more hardware-oriented fashion). 
         [0043]    The computer system  100 , as depicted in  FIG. 3 , includes the computer  201  of  FIG. 2 . The computer  201  is depicted as a “referral server” in  FIG. 3 , given that a function of the computer  201  is to selectively refer current claim transactions to an investigation unit of the insurance company. As seen from  FIG. 3 , the computer system  100  may further include a conventional data communication network  302  to which the computer/referral server  201  is coupled. 
         [0044]      FIG. 3  also shows, as parts of computer system  100 , data input device(s)  304  and data source(s)  306 , the latter (and possibly also the former) being coupled to the data communication network  302 . The data input device(s)  304  and the data source(s)  306  may collectively include the devices  108 ,  110  and  112  discussed above with reference to  FIG. 1 . More generally, the data input device(s)  304  and the data source(s)  306  may encompass any and all devices conventionally used, or hereafter proposed for use, in gathering, inputting, receiving and/or storing information for insurance company claim files. 
         [0045]    Still further,  FIG. 3  shows, as parts of the computer system  100 , personal computers  308  assigned for use by investigative analysts (who are members of the insurance company&#39;s investigation unit) and personal computers  310  assigned for use by investigators (also members of the investigation unit). The personal computers  308 ,  310  are coupled to the data communication network  302 . 
         [0046]    Also included in the computer system  100 , and coupled to the data communication network  302 , is an electronic mail server computer  312 . The electronic mail server computer  312  provides a capability for electronic mail messages to be exchanged among the other devices coupled to the data communication network  302 . 
         [0047]    Thus the electronic mail server computer  312  may be part of an electronic mail system included in the computer system  100 . 
         [0048]    The computer system  100  may also be considered to include further personal computers (not shown), including, e.g., computers which are assigned to individual claim handlers or other employees of the insurance company. 
         [0049]      FIG. 4  is a flow chart that illustrates a process that may be performed in the computer system  100 /computer  201  of  FIGS. 1-3 . 
         [0050]    At  402  in  FIG. 4 , determinate data concerning historical claim transactions is provided to the computer system  100 /computer  201 . As indicated above, the determinate data may reflect the “hard facts” about the historical claim transactions. In addition, the determinate data may include, for each historical claim transaction, an indication as to whether or not the transaction in question was determined to be proper. It will be understood that the historical claim transactions relate to claims made during a past period of time or periods of time. This information may, for example, be loaded into the computer  201  from a database and/or archive of claim files. 
         [0051]    At  404 , the determinate historical claim transaction data may be stored in the computer system  100 /computer  201 . 
         [0052]    At  406 , indeterminate data for the historical claim transactions is provided to the computer system  100 /computer  201 . The indeterminate data may take the form, for example, of images of paper documents, digitized voice recordings, text files that reflect claim handlers&#39; case file notes, or text files generated by OCR-processing paper documents or document images, or text files generated by speech-to-text conversion of digitized voice recordings. 
         [0053]    At  408 , the indeterminate historical claim transaction data may be stored in the computer system  100 /computer  201 . 
         [0054]    At  410  the computer system  100 /computer  201  performs processing to extract significant information from the stored indeterminate historical claim transaction data. The extraction of information from the indeterminate historical claim transaction data may take one or more of a number of different forms. For example, the computer system  100 /computer  201  may perform key word searches on text files included in the indeterminate historical claim transaction data. As another example, the computer system  100 /computer  201  may perform natural language processing on the text files included in the indeterminate historical claim transaction data, to determine whether those text files contain, in substance, one or more of a number of different types of statements. (E.g., “employer disputes claim” (for a worker&#39;s compensation claim), or “witness saw claimant engaging in physical activity”, or “claimant&#39;s statements are inconsistent”.) As still another example, names of entities (e.g., the treating physician&#39;s name) may be extracted from text files (e.g., claimant&#39;s attorney&#39;s demand package after OCR scanning) by entity analysis of the text files. As yet another example, a digitized recorded voice file may be analyzed to detect the speaker&#39;s (e.g., the claimant&#39;s) state of mind. This may be done, for example, by use of commercially available software packages and/or in accordance with teachings of U.S. Published Patent Application No. 2003/0033145 (which names Petrushin as inventor). 
         [0055]    One example of natural language processing may operate to mine claim handlers&#39; opinions from the narrative text notes. 
         [0056]    At  412  in  FIG. 4 , the information extracted at  410  from the indeterminate historical claim transaction data is stored by the computer system  100 /computer  201 . 
         [0057]    At  414 , two mutually exclusive subsets of the historical claim transactions are formed. One of the two subsets is to be used for training the predictive model. The other of the two subsets is to be used for verifying the predictive model after it has been trained. 
         [0058]    At  416 , the predictive model is trained using the first one of the subsets of historical claim transaction data. In some embodiments, for example, the predictive model may be trained by using one or more types of regression analysis. In addition or alternatively, other types of pattern detection analysis may be applied to the first subset of historical claim transaction data. As another alternative, the predictive model may be implemented as a neural network. In some embodiments, the predictive model is of a kind that, for each claim transaction (after training) to which it is applied, the model generates a numerical output within a certain range. The output may be generated in such a manner that a higher output implies a higher likelihood that it would be worthwhile to refer the claim for special investigation. 
         [0059]    After training of the predictive model, it is verified, as indicated at  418 , by applying the model to the second subset of historical claim transaction data. The results of the verification processing are analyzed to confirm that the predictive model performs effectively in generally assigning higher outputs to the claim transactions in the second subset that actually had been determined to be improper. 
         [0060]    At  420  in  FIG. 4 , determinate data concerning current claim transactions is provided to the computer system  100 /computer  201 . Preferably the determinate data for the current claim transactions is of the same kinds as the determinate data for the historical claim transactions, as described above in connection with step  402 . The determinate data may be loaded into the computer  201  from a database of current claim information. 
         [0061]    At  422 , the determinate current claim transaction data may be stored in the computer system  100 /computer  201 . 
         [0062]    At  424 , indeterminate data for the current claim transactions is provided to the computer system  100 /computer  201 . Preferably the indeterminate data for the current claim transactions is of the same kinds as the indeterminate data for the historical claim transactions, as described above in connection with step  406 . 
         [0063]    At  426 , the indeterminate current claim transaction data may be stored in the computer system  100 /computer  201 . 
         [0064]    At  428 , the computer system  100 /computer  201  performs processing to extract significant information from the stored indeterminate current claim transaction data. Preferably this is done in the same manner as the information extraction described above in connection with step  410 . 
         [0065]    At  430 , the information extracted at  428  from the indeterminate current claim transaction data is stored by the computer system  100 /computer  201 . 
         [0066]    Referring now to  FIG. 4 , at  432  the predictive model is applied to the determinate data, and to the information extracted at  428  from the indeterminate data, for one of the current claim transactions. (One could also say that the determinate data and the information extracted from the indeterminate data are applied to the predictive model.) The operation of the predictive model results in an output being generated for the current claim transaction in question. As indicated by previous discussion, the output is indicative of a determination by the predictive model as to the likelihood that the current claim transaction should be identified as potentially questionable, with a higher output indicating a greater likelihood that the claim is questionable. 
         [0067]    At  434  in  FIG. 4 , the computer  201  makes a routing decision with respect to the current claim transaction applied to the predictive model at  432 . This decision is based on the output generated from the predictive model for the current claim transaction in question. From ensuing discussion, it will be understood that the routing decision may be whether to refer the current claim transaction in question to the insurance company&#39;s investigation unit. 
         [0068]      FIGS. 5 and 6  are flow charts that illustrate additional details of the process of  FIG. 4 . 
         [0069]    Referring initially to  FIG. 5 , at  502  data for the next (or first) current claim transaction in a batch of transactions to be screened is accessed. Then, at decision block  504  the computer system  100 /computer  201  determines whether the current claim transaction accessed at  502  qualifies for consideration for referral to the investigations unit. That is, certain current claim transactions may fall into categories such that the current claim transaction is highly unlikely to be questionable, hence not worth analyzing by the predictive model. Examples of current claim transactions that are highly unlikely to be questionable may include, for example, worker&#39;s compensation claims arising from loss of life on the job, or from occupational illness. Thus decision block  504  may entail determining whether the current claim transaction accessed at  502  falls into one of the categories that are highly unlikely to be questionable. It may be said that decision block  504  performs a “pre-screening” relative to the current claim transaction accessed at block  502 . 
         [0070]    If a determination is made at decision block  504  to the effect that it makes sense to apply the current claim transaction accessed at  502  to the predictive model, then block  506  may follow decision block  504 . At block  506 , the current claim transaction accessed at  502  is placed in a queue for application of the predictive model. Then decision block  508  follows block  506 . At decision block  508 , it is determined whether any more current claim transactions are awaiting analysis by the predictive model. If so, then the process of  FIG. 5  loops back to block  502 , and the next current claim transaction awaiting screening is accessed and subjected to “pre-screening” at decision block  504 . However, if at decision block  508  it is determined that no further current claim transactions are awaiting analysis by the predictive model, then the process of  FIG. 5  ends. 
         [0071]    Considering again decision block  504 , if a determination is made at that point that the current claim transaction accessed at block  502  is of a kind such that it does not make sense to apply the predictive model, then, per branch  510 , the process of  FIG. 5  skips from decision block  504  to decision block  508 , omitting block  506  with respect to the current claim transaction accessed at block  502 . Thus, in such cases, the current claim transaction accessed at block  502  is not queued for application of the predictive model and is not screened for possible referral to the investigation unit. 
         [0072]    Referring now to  FIG. 6 , the process illustrated therein begins with decision block  602 . At decision block  602  the computer system  100 /computer  201  determines whether there are any current claim transactions in the queue referred to in connection with block  506  of  FIG. 5 . If there is at least one current claim transaction in the queue, then block  604  follows decision block  602 . At  604 , the computer system  100 /computer  201  accesses the next current claim transaction in the queue. Then, at  606 , the computer system  100 /computer  201  applies the predictive model to the current claim transaction accessed at  604 . From previous discussion it will be recognized that the application of the predictive model to the current claim transaction in question results in the computer system  100 /computer  201  generating an output for the current claim transaction in question, where the output is indicative of whether it is advisable that the current claim transaction in question should be referred to the investigation unit. 
         [0073]    Decision block  608  follows block  606 . At decision block  608 , the computer system  100 /computer  201  determines whether the output generated at  606  exceeds a predetermined threshold. The threshold may, for example, have been set at the time that the training of the predictive model was verified (block  418 ,  FIG. 4 ). The threshold may have been set in such a manner as to balance the potentially conflicting goals of avoiding false positive indications, while avoiding false negative determinations. The achievement of this balance may reflect the respective levels of outputs generated by the predictive model during verification processing to proper and improper historical claim transactions. 
         [0074]    If it is determined at decision block  608  that the output generated at  606  exceeds the threshold, then block  610  follows decision block  608 . At block  610 , the computer system  100 /computer  201  may refer the current claim transaction in question to an investigative analyst in the insurance company&#39;s investigation unit. This may be done by the computer system  100 /computer  201  automatically e-mailing the electronic case file for the claim transaction to the investigative analyst. If more than one investigative analyst is available to receive the referral of the claim transaction, then the computer system  100 /computer  201  may automatically select the investigative analyst who is to receive the referral based on one or more factors such as the type of claim, the investigative analyst&#39;s qualifications and/or experience, the investigative analyst&#39;s current workload, etc. The investigative analyst&#39;s role, at this point, is to review the claim transaction, confirm that the referral is warranted, proceed with a desk analysis/investigation of the claim transaction, and, if field investigation is then found to be warranted, refer the claim transaction on to a field investigator for further investigation. 
         [0075]    In some embodiments, the computer system  100 /computer  201  may cause the claim transactions referred to each investigative analyst, and/or to the investigation unit as a whole, to be queued according to the outputs generated for the claim transactions. That is, claim transactions having higher outputs assigned by the predictive model may be placed higher in the individual investigative analysts&#39; queues and/or in the investigation unit&#39;s queue. 
         [0076]    Block  612  may follow block  610 . At block  612  the computer system  100 /computer  201  automatically notifies the claim handler to whom the claim transaction had been assigned that the claim transaction was being referred to the investigation unit and that the claim handler should suspend processing of the claim transaction. At the same time, the computer system  100 /computer  201  may inform other interested branches of the insurance company that the claim transaction is being referred to the investigation unit. 
         [0077]    After block  612 , the process of  FIG. 6  loops back to decision block  602  to determine whether there are other claim transactions in the queue to be analyzed by the predictive model. If, at a point when decision block  602  is reached, there are no more claim transactions in the queue, then the process of  FIG. 6  ends, as indicated at branch  614 . 
         [0078]    As noted above, in some embodiments, when the predictive model indicates that a current claim transaction should be referred to the investigation unit, the current claim transaction may be referred to an investigative analyst. In other embodiments, however, the current claim transaction may be referred directly to a field investigator. Nevertheless, it may be preferable to make claim transaction referrals for investigation by an investigative analyst. The investigative analyst may take steps such as confirming that the referral for investigation is appropriate, planning and executing a “desk investigation” of the claim transaction, and making a further referral to a field investigator if warranted. If the referral for investigation was not appropriate, the investigative analyst may return the claim transaction to the claim handler without investigating the claim transaction (possibly with an explanation why the investigative analyst considered that the claim transaction should not be investigated by the investigation unit). If the referral was appropriate, the investigative analyst may proceed with a desk investigation, and may report the results of his/her investigation to the claim handler. 
         [0079]    Referrals for investigation by an investigative analyst may prove more economical than referrals directly to a field investigator. Typically investigation by an investigative analyst may be less costly than investigation by a field investigator. Also, an investigative analyst may be more skilled and experienced at desk investigation techniques than a field investigator. Involvement by an investigative analyst may help to conserve, and use more efficiently, potentially expensive and/or scarce field investigator resources. 
         [0080]    The investigation unit may perform various types of investigations, including investigations related to subrogation or possible fraud. 
         [0081]    In some embodiments, the investigation unit may receive referrals via other channels than referral based on a predictive model. For example, referrals may also be from claim handlers, or from outside sources, such as the National Insurance Crime Bureau (NICB), directly from insurance agents, etc. Other referrals for investigation may take place selectively/pro-actively based on other sources when suspicious claim transactions or patterns of questionable claim transactions are identified. 
         [0082]    In some embodiments, some or all of the above-mentioned communications among investigative analysts, claim handlers, and field investigators may be via the electronic mail system referred to above in conjunction with  FIG. 3 . 
         [0083]    The process descriptions and flow charts contained herein should not be considered to imply a fixed order for performing process steps. Rather, process steps may be performed in any order that is practicable. 
         [0084]    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.