Patent Publication Number: US-2012030076-A1

Title: System and method for risk-based data assessment

Description:
RELATED APPLICATIONS 
     This application is related to, and incorporates by references in their entirety, U.S. patent application Ser. No. ______, filed Jul. 29, 2010, entitled “RISK SCORING SYSTEM AND METHOD FOR RISK-BASED DATA ASSESSMENT,” by Benjamin Anthony Slater and Christopher P. Checco (Attorney Docket No. 10761.2361-00000), and U.S. patent application Ser. No. ______, filed Jul. 29, 2010, entitled “SYSTEMIC RISK MONITORING SYSTEM AND METHOD FOR REVENUE AGENCIES,” by Benjamin Anthony Slater and Christopher P. Checco (Attorney Docket No. 10761.2362-00000). 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to a system and method for data assessment and, more particularly, to a system and method for collection, analysis, and transformation of data into a format suitable for analytics use, such as statistical risk-based assessments. 
     BACKGROUND 
     Taxpayers, such as, individuals and companies, may file income tax returns yearly. Based on a taxpayer&#39;s gross income and possible deductions and exemptions available, a taxpayer may either owe taxes or receive a refund for taxes paid during the year. Accordingly, it is common for a taxpayer to attempt to reduce his taxable income, and associated income tax liability, such that a taxpayer is required to pay a minimum amount of taxes. 
     In order to reduce the amount of income tax liability, taxpayers have been known to resort to fraudulent actions. Fraudulent actions may include, for example, deducting expenses that are not legally available, claiming more dependents than allowed by law, and failing to report all earned income. Other examples of fraudulent activity may include purchasing or stealing one or more false identities and filing fraudulent tax returns in an effort to receive a tax refund. 
     While these examples of fraudulent activities are prevalent, they may be difficult to identify by the tax receiving agency due to the volume of tax returns filed with the agency or for other reasons. For example, over 130 million tax returns were filed with the United States tax agency, the Internal Revenue Service (“IRS”), in 2007. A corresponding number of tax returns were also filed in the corresponding state(s) of residence for each taxpayer. Within this large volume of filed tax returns are a large number of fraudulent tax returns that either improperly reduce the correct amount of taxes owed or improperly increase the amount of refunds owed. 
     One way to identify a fraudulent tax return would be to audit the return when it is received, e.g., by comparing it to previously-filed returns, historical data, etc. When applying analytics to determine a fraud risk of a tax return, it is desirable to apply one or more risk rules and/or statistical algorithms to data contained in the tax return and/or a broad range of stored historical data. Current systems typically conduct this type of risk scoring at the time the tax return is received. 
     In current systems, however, this type of risk scoring is costly in terms of time and resources. Accordingly, the ability to employ analytic techniques is often limited by the need to maintain very high standards of performance and reliability in the transaction processing system because sophisticated analytics require substantial processing power and add substantial complexity if executed in real time as a part of the transaction processing. 
     SUMMARY 
     In accordance with the present disclosure, as embodied and broadly described herein, a method of preparing data for performing analytic-based data assessment comprises: receiving data from one or more data sources; generating one or more associations among the received data, the associations providing links between one or more data elements of the received data; generating one or more segmented data sets based on the received data and the one or more associations, the segmented data sets reflecting an organization of the received data; integrating one or more variables related to the received data into the one or more segmented data sets to create pre-processed data; and outputting the pre-processed data and integrated real-time data for application of analytics. 
     In accordance with the present disclosure, as embodied and broadly described herein, a computer-readable recording medium storing a computer-executable program which, when executed by a processor, performs a method of preparing data for analytic-based data assessment, comprises: receiving data from one or more data sources; generating one or more associations among the received data, the associations providing links between one or more data elements of the received data; generating one or more segmented data sets based on the received data and the one or more associations, the segmented data sets reflecting an organization of the received data; integrating one or more variables related to the received data into the one or more segmented data sets to create pre-processed data; and outputting the pre-processed data and integrated real-time data for application of analytics. 
     In accordance with the present disclosure, as embodied and broadly described herein, a system of preparing data for performing analytic-based data assessment, the system comprises: at least one memory to store data and instructions; and at least one processor configured to access the at least one memory and, when executing the instructions, to: receive data from one or more data sources; generate one or more associations among the received data, the associations providing links between one or more data elements of the received data; generate one or more segmented data sets based on the received data and the one or more associations, the segmented data sets reflecting an organization of the received data; integrate one or more variables related to the received data into the one or more segmented data sets to create pre-processed data; and output the pre-processed data and integrated real-time data for application of analytics. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments and aspects of the present disclosure. In the drawings: 
         FIG. 1  illustrates an exemplary computing system for risk-based data assessment, consistent with certain disclosed embodiments; 
         FIG. 2  illustrates is a schematic view of an exemplary risk scoring engine, consistent with certain disclosed embodiments; 
         FIG. 3  illustrates a flowchart of an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments; 
         FIG. 4  illustrates an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments; 
         FIG. 5  illustrates an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments; 
         FIG. 6  illustrates an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments; 
         FIG. 7  illustrates an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments; and 
         FIG. 8  illustrates an exemplary data creation process for an exemplary method of risk-based data assessment, consistent with certain exemplary embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several exemplary embodiments and features are described herein, modifications, adaptations and other implementations are possible, without departing from the spirit and scope of the disclosure. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the exemplary methods described herein may be modified by substituting, reordering or adding steps to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. 
     Systems and methods consistent with the present disclosure move complexity from the time of tax return processing to a preprocessing stage which simplifies the creation of analytical data during tax return processing, thus improving performance and reliability. Further, certain embodiments use a series of data structures or risk profiles corresponding to one or more entities to allow pre-calculated risk-relevant information to be stored and used to determine a risk score for a particular transaction, such as a tax return. When a transaction requires risk scoring, a risk scoring engine may determine the particular profile(s) related to the transaction, and gather the profiles and transaction together to provide the necessary data to risk scoring rules. For example, when a tax return is to be processed, the pre-processing manager may gather related profiles, e.g., for a taxpayer, a tax preparer, etc. 
     In this manner, systems consistent with the present disclosure may reduce complexity, thereby improving performance and reliability, in risk scoring that occurs at the time a transaction is received without reducing the ability to apply sophisticated analytic techniques to detect fraud. 
     By way of a non-limiting example,  FIG. 1  illustrates a system  100  in which the features and principles of the present disclosure may be implemented. The number of components in system  100  is not limited to what is shown, and other variations in the number and/or arrangements of components are possible, consistent with embodiments of the disclosure. The components of  FIG. 1  may be implemented through hardware, software, firmware, etc. System  100  may include clients  120  (e.g., client  120   a , client  120   b , through client  120   n ), risk scoring engine  110 , data sources  130  (e.g., data source  130   a , data source  130   b , through data source  130   n  and network  140 . 
     Clients  120  may each include one or more apparatus configured to provide one or more users with an interface to network  140 . By way of example, clients  120  may be implemented using any device capable of accessing a data network, such as, for example, a general purpose computer or personal computer equipped with a modem or other network interface. Clients  120  may also be implemented in other devices, such as, for example, laptop computers, desktop computers, mobile phones (with data access functions), Personal Digital Assistant (“PDA”) with a network connection, IP telephony phone, or generally any device capable of communicating over a data network, such as, for example, network  140 . 
     In some embodiments, clients  120  may be configured to transmit and/or receive data to/from risk scoring engine  110 . Data may be entered into and/or stored on one or more clients  120 . The data may include, for example, tax return data which a user may enter into client  120 . After the user enters this information, client  120  may transmit the data to risk scoring engine  110 , which in turn may receive the data, as well as store and/or analyze the data. 
     Data sources  130  may include one or more sources of data, including databases, data entry systems, document collection devices, etc. In some disclosed embodiments, data sources  130  may organize and store data collected for performing one or more methods of data collection, analysis, and transformation of data for risk-based assessment and risk scoring. In some embodiments, data sources  130  may include data previously received by risk scoring engine  110  (i.e., historical data). 
     Data provided by data sources  130  may include data corresponding to any type of information, including, for example, demographic data, credit and/or financial data (e.g., credit bureau information, banking information, credit union information, lender information, etc.), employer and/or employee data (e.g., employer name, employer taxpayer identification number, employer address, taxable income, identification of employees, distributions to employees and/or government agencies, etc.), tax data (e.g., a taxpayer&#39;s name, address, social security number, tax ID number, taxable income, number of exemptions, deductions, tax credits, etc.), government data sources, publically-available data sources (e.g., GOOGLE™, etc.), commercial data sources (e.g., LEXIS NEXIS™, etc.), etc. In addition, data sources  130  may include one or more database managements systems, which store database contents, allow data creation and maintenance, perform searches, provide security and backup, and allow other data accesses. 
     Risk scoring engine  110  may provide a platform for exchanging (e.g., sending and/or receiving) data with clients  120  and/or exchanging (e.g., sending and/or receiving) data with data sources  130 . Risk scoring engine  110  may be implemented using a combination of hardware, software, firmware, etc., and may be operable to receive and store data from various clients  120 . In some embodiments, risk scoring engine  110  may receive data from clients  120  regarding tax information. In addition, risk scoring engine  110  may also generate one or more alerts regarding possible fraud, for example, based on the data received in connection with one or more clients  120 . In an embodiment, the functionality of risk scoring engine  110  may be implemented on a single physical chassis. In an alternative embodiment, the functionality of risk scoring engine  110  may be distributed amongst a plurality of physical chassis without departing from the scope of this disclosure. Additionally, in some embodiments, risk scoring engine  110  may be operated and/or implemented by a tax authority (e.g., the IRS, etc.). In other embodiments, risk scoring engine  110  may be operated and/or implemented by a third party vendor in support of the tax authority. 
     Network  140  provides communication between or among the various entities depicted in system  100 . Network  140  may be a shared, public, or private network and may encompass a wide area network (WAN), local area network (LAN), an intranet, and/or the Internet. Network  140  may be implemented through any suitable combination of wired and/or wireless communication networks (including Wi-Fi networks, GSM/GPRS networks, TDMA networks, CDMA networks, Bluetooth networks, or any other wireless networks. Further, the entities of system  100  may be connected to multiple networks  140 , such as, for example, to a wireless carrier network, a private data network, and the public Internet. 
       FIG. 2  is a schematic view of risk scoring engine  110 , consistent with certain disclosed embodiments. As discussed above, risk scoring engine  110  may be operated and/or implemented by a tax authority and/or a third party vendor in support of the tax authority to perform risk-based analysis of tax data. As shown in  FIG. 2 , risk scoring engine  110  may include one or more of the following components: at least one central processing unit (CPU)  201  (also referred to herein as a processor) configured to execute computer program instructions to perform processes and methods consistent with the disclosed exemplary embodiments, random access memory (RAM)  202  and read only memory (ROM)  203  configured to access and store information and computer program instructions, cache  204  to store data and information, one or more databases  205  to store tables, lists, or other data structures, I/O interfaces  206  (including, for example, interfaces to network  140 , one or more displays (not shown) one or more printers (not shown), one or more keyboards (not shown), etc.), software and firmware interfaces  207  (including, for example, interfaces to one or more user interfaces, etc.), antennas  208  for wireless transmission and/or reception of data and/or other information, etc. 
       FIG. 3  is an exemplary flowchart  300  illustrating a process for risk-based data processing in a risk scoring engine, such as, for example, risk scoring engine  110 , in accordance with certain implementations. Specifically,  FIG. 3  illustrates an implementation in which central processing unit  201  of risk scoring engine  110  performs data collection, analysis, and transformation for risk scoring and risk-based assessments. For example, a tax collection agency or a third party provider may implement the process illustrated by flowchart  300  to prepare data for tax return processing, etc. Although the steps of the flowchart are described in a particular order, one skilled in the art will appreciate that these steps may be performed in a modified or different order, or that certain steps may be omitted or other steps added. Further, one or more of the steps in  FIG. 3  may be performed concurrently or in parallel. 
     As shown in  FIG. 3 , risk scoring engine  110  may receive data from one or more data sources ( 305 ). The one or more data sources may include, for example, one or more data sources  130 , as described above in connection with  FIG. 1 . For example, data sources  130  may include historical tax return data associated with a taxpayer. In some embodiments, risk scoring engine  110  may send a query to data sources  130 , requesting the data. Alternatively and/or additionally, data sources  130  may send data to risk scoring engine  110  automatically, including, for example, at predetermined time (e.g., daily, weekly, monthly, etc.) or when predetermined conditions have been met (e.g., a predetermined amount of data has been collected, a predetermined threshold has been met, a predetermined triggering event has occurred, etc.). The received data may include, for example, demographic data, credit and/or financial data, employer and/or employee data, tax data, data compilations, etc. In addition, the received data may include unstructured data, data from documents received via one or more clients  120 , and/or any other type of data. The received data may be stored in one or more storage locations of risk scoring engine  110 , such as one or more databases  205 . 
       FIG. 4  is a diagram illustrating a process for receiving data by risk scoring engine  110 , and storing the received data in database  205  of risk scoring engine  110 , consistent with certain disclosed embodiments. In  FIG. 4 , data from multiple, disparate sources is staged such that initial data profiling and data cleansing may be applied. As shown in  FIG. 4 , one or more external data sources  130  may send data to risk scoring engine  110 . Risk scoring engine  110  may, in turn, store the received data in one or more databases  205 . As illustrated in  FIG. 4 , the received data may include any type of data including unstructured data, commercial data, document collections, public data, etc. 
     Referring again to  FIG. 3 , risk scoring engine  110  may review and organize the received data ( 310 ). Reviewing and organizing the received data may, for example, include initial data profiling and data cleansing based on one or more data elements of the received data. 
     Initial data profiling may include compiling the received data into one or more data element groups and/or data element ranges. For example, if the received data includes tax data, the one or more data element groups may include tax filing status (e.g., single, married, head of household, etc.) and the one or more data element ranges may include ranges of adjusted gross income. Cleansing the received data may include, for example, identifying any data elements that may be in a non-standard or non-preferred format, and changing the data elements to a standard or preferred format. For example, United States ZIP code data elements having five digit ZIP codes may be changed to nine digit ZIP codes (so-called “ZIP+4”). As another example, null values in certain data elements may be changed to predetermined values, which may include zero values. Risk scoring engine  110  may store the reviewed and organized data in, for example, one or more databases  205 . In other embodiments, risk scoring engine  110  may provide the one or more reviewed and organized data to one or more external data sources  130  and/or in response to requests from one or more users. 
     Risk scoring engine  110  may identify and generate associations among the received data ( 315 ). The associations may be identified and generated within data received from a single data source  130  and/or between data received from more than one data source  130 . Risk scoring engine  110  may store the generated associations as links to identify relationships between data elements. Associations may be generated to identify one or more relationships between one or more source-centric data views and one or more entity-centric data views. 
     Source-centric data views may be the data views associated with the source from which the data was received (e.g., data sources  130 ). Entity-centric data views may be data sets associated with an entity about which the data may relate. For example, in a tax embodiment, the entities may include, for example, one or more tax returns, one or more taxpayers, one or more tax preparers, one or more employers, etc. As such, an entity-centric data view for a taxpayer may include a view of all data associated with a taxpayer, e.g., the taxpayer&#39;s name, address, social security number, occupation, etc. The entity-centric data view for a tax preparer may include, for example, information regarding the entity who prepared the taxpayer&#39;s tax return, such as, for example, an individual preparer, a tax preparation service, a tax preparation company, etc. The entity-centric data view for a taxpayer&#39;s employer may include, for example, information regarding the taxpayer&#39;s one or more employers for the tax return. Thus, for example, associations between a source-centric view of a taxpayer and an entity-centric data view of historical data may include a taxpayer&#39;s name and social security number. 
       FIG. 5  is a diagram illustrating a process of generating associations among received data and the transformation from source-centric views to entity-centric views, consistent with certain disclosed embodiments. As shown in  FIG. 5 , in one exemplary embodiment, risk scoring engine  110  may identify data elements contained within the received data and create associations between one or more data elements of source-centric data views  510  (e.g., data view  510   a , data view  510   b , data view  510   c , through data view  510   n ) and one or more data elements of entity-centric data views  520  (e.g., tax return data view  520   a , taxpayer data view  520   b , tax preparer data view  520   c , taxpayer&#39;s employer data view  520   d , etc.). For example, risk scoring engine  110  may identify and generate associations between one or more data elements of source-centric data view  510   a  and one or more data elements of each of entity-centric data views  520   a ,  520   b ,  520   c , and  520   d . In addition, risk scoring engine  110  may identify and generate associations between one or more data elements of source-centric data view  510   b  and one or more data elements of each of entity-centric data views  520   a ,  520   b ,  520   c , and  520   d , as well as identify and generate associations between one or more data elements of source-centric data views  510   c  through  510   n  and one or more data elements of each of entity-centric data views  520   a ,  520   b ,  520   c , and  520   d . Risk scoring engine  110  may store the identified and generated associations as links in, for example, a database management system of database  205 . In this manner, according to one exemplary embodiment, the received data may be transformed from source-centric data views to entity-centric data views, readying the data for analytic modeling. 
     Referring again to  FIG. 3 , risk scoring engine  110  may create segmented data sets based on the received data ( 320 ). Segmented data sets may be data sets that are either logically and/or physically divided, separated, organized, and/or sectioned from one another. In one exemplary embodiment, segmented data sets may be data sets that are logically organized to be distinct from other data sets. The segmented data sets may be used to identify changes in behavior and/or trending. In addition, the segmented data sets may assist in identifying changes in normative values, including intra-entity normative values (e.g., for a single return, for a single taxpayer, for a single tax preparer, for a single employer, etc.), inter-entity normative values (e.g., among a set of returns for a single taxpayer, among a set of returns for a set of taxpayers, among a set of taxpayers, among a set of taxpayers associated with a single employer, among a set of taxpayers associated with a single tax preparer, among a set of tax preparers, among a set of employers, etc.), etc. 
     In some embodiments, the segmented data sets may be temporally-segmented data sets (e.g., daily, weekly, monthly, annually, etc.) for each of one or more entities (e.g., returns, taxpayer, tax preparer, employer, etc.). Temporally-segmented data sets may be aggregated to larger time segments. In some embodiments, the temporally-segmented data may be aggregated for a single entity, multiple entities, or any combination thereof. 
       FIG. 6  illustrates one exemplary embodiment of temporally-segmented data sets, consistent with certain disclosed embodiments. In an implementation for processing tax returns, the entity-centric data views  520  may include a taxpayer data view  520   b , a tax preparer data view  520   c , and a taxpayer&#39;s employer data view  520   d . The temporally-segmented data sets for a taxpayer (i.e., taxpayer temporally-segmented data sets  610   b ) may include, for example, a current view (e.g., the current tax return), an annual view (e.g., tax data received over the course of a year from, for example, the taxpayer&#39;s employer, etc.), and a lifetime view (e.g., every tax return a taxpayer has filed in their lifetime or the lifetime of the electronically-available data, or subsets thereof). The temporally-segmented data sets for a tax preparer (i.e., tax preparer temporally-segmented data sets  610   c ) may include, for example, a daily view (e.g., data related to all tax returns filed in a given day), a weekly view (e.g., data related to all tax returns filed in a given week), a monthly view (e.g., data related to all tax returns filed in a given month), an annual view (e.g., data related to all tax returns filed in a given year), and a lifetime view (e.g., every tax return filed by a tax preparer in his/her lifetime or the lifetime of the electronically-available data). The temporally-segmented data sets for a taxpayer&#39;s employer (i.e., taxpayer&#39;s employer temporally-segmented data sets  610   d ) may include, for example, a daily view (e.g., tax data related to employees reported to a tax authority in a given day), a weekly view (e.g., tax data related to employees reported to a tax authority in a given week), a monthly view (e.g., tax data related to employees reported to a tax authority in a given month), an annual view (e.g., tax data related to employees reported to a tax authority in a given year), and a lifetime view (e.g., tax data related to employees reported to a tax authority in a lifetime of the taxpayer&#39;s employer or the lifetime of the electronically-available data). 
     Returning to  FIG. 3 , risk scoring engine  110  may integrate one or more transformed variables with the segmented data sets, such as the temporally-segmented data sets, to generate pre-processed data ( 325 ). Generating pre-processed data may increase the speed by which tax return data may be assessed, including risk-based assessment and risk scoring, consistent with the disclosed embodiments. In some embodiments, the transformed variables may replace one or more other data elements in the segmented data sets. In other embodiments, the transformed variables may supplement the data of the segmented data sets. 
     The one or more transformed variables may include, for example, categorical variables, calculated variables, trend variables, lifetime variables, etc. Raw variables may include any type of raw data received from one or more data sources  130 . Categorical variables may include data associated with one or more categories of information. Examples of categorical variables may include a number of tax preparers associated with a tax preparation company, number of years a tax preparer has been in business, type of company (e.g., S-corporation, C-corporation, etc.), etc. Calculated variables may include any type of data obtained via simple calculations (e.g., addition, subtraction, etc.). Examples of calculated variables may include a total number of tax returns filed by a tax preparer, a total number of tax returns filed by a tax preparer within a certain time period, a total number of tax returns having a particular type of tax deduction, etc. Trend variables may include a summarization of data value changes over a specified period of time as compared to another predetermined period of time. Examples of trend variables include a total number of tax returns filed over the current week as compared to the total number of tax returns filed over the previous week, a value reflecting a rate of change in gross income from the current year versus the average gross income from the previous five years (e.g., increase, decrease, etc.), etc. Lifetime variables may include data corresponding to total values associated with an entity over a lifetime or, in the case of electronically-stored data, the lifetime of the stored data for one or more data elements, or a rolling predetermined window of time that can encapsulate all data equally (e.g., five year rolling window, etc.). Examples of lifetime variables may include a total number of tax returns filed by a taxpayer over the entity and/or stored data lifetime, a total number of tax returns filed by a tax preparer over the entity and/or stored data lifetime, a total number of tax returns having a particular type of tax deduction over the entity lifetime and/or stored data lifetime, etc. 
       FIG. 7  illustrates one exemplary embodiment of integrating transformed variables with temporally-segmented data sets  610 , consistent with certain disclosed embodiments. In  FIG. 7 , for each time period (i.e., Period #1, Period #2, . . . , Period #n), the temporally-segmented data sets  610  associated with each entity-centric data view  520  are integrated with one or more transformed variables  710 , including one or more raw variables, categorical variables, calculated variables, trend variables, and lifetime variables for each time period such that each entity has one record per time period. For example, the temporally-segmented data sets  610   b  are integrated with the one or more transformed variables for each time period, the temporally-segmented data sets  610   c  are integrated with the one or more transformed variables for each time period, the temporally-segmented data sets  610   d  are integrated with the one or more transformed variables for each time period, and so on. The resulting pre-processed data is then available for further processing and/or combination with real-time data. 
     Referring again to  FIG. 3 , risk scoring engine  110  outputs the pre-processed data in combination with integrated real-time data ( 330 ). In some embodiments, the combined data may be output and/or stored by database  205  for subsequent use by one or more other processes. In other embodiments, the combined data may be output to one or more I/O devices, including, for example, displays, printers, etc. In still other embodiments, the combined data may be output to one or more other computing devices, such as, for example, handheld electronic devices, mobile telephones, PDAs, etc. In some embodiments, the real-time data may be transformed as a result of its integration with the pre-processed data. 
       FIG. 8  illustrates one exemplary embodiment of outputting the pre-processed data  810  in combination with integrated real-time data  820 , consistent with certain disclosed embodiments. In  FIG. 8 , the pre-processed data  810 , consisting of temporally-segmented data sets which are integrated with one or more transformed variables, are output in connection with integrated real-time data  820 . Integrated real-time data  820  may include current year tax data that may, in the disclosed implementations, be realized by risk scoring engine  110  as data views  520  when a tax return (e.g., tax return data view  520   a ) associated with a taxpayer (e.g., taxpayer data view  520   b ) is prepared and submitted by a tax preparer (e.g., tax preparer data view  520   c ), that further identifies the taxpayer&#39;s employer (e.g., taxpayer&#39;s employer data view  520   d ). 
     The disclosed embodiments may be used, for example, to determine if there is a potential for risk or potential opportunity/optimization associated with an action and/or activity, such as, for example, risk of fraud, financial risk, attrition risk, cross-sell opportunities, etc. For example, the disclosed embodiments may be used to analyze the same or substantially similar tax returns to determine if there is a risk of fraud associated with these tax returns. In addition, the disclosed embodiments may be used to generate risk scores such that different types of risk may have different risk scores. Each risk score may be compared with a risk score threshold that may be predetermined or customized. For example, using the disclosed embodiments, each tax season different risk score thresholds may be determined. Conversely, risk score thresholds may be used, unchanged, between tax seasons. When a risk score exceeds some predetermined threshold, a flag may be generated such that one or more alerts are provided to one or more appropriate users and/or entities who may, in turn, conduct a more detailed analysis of the identified risk, and determine if the taxpayer is attempting to fraudulently file the tax return. 
     To provide a simple, notional example, the average value of income for a taxpayer over the last 5 years and the average value of income for all taxpayers who have used a particular prepare in the last 5 years may be relevant to determining the risk associated with a particular tax return. Through the embodiments disclosed herein, these two averages may be calculated and stored in risk profiles for the taxpayer and prepare before the particular tax return arrives for processing. Rather than retrieving the last 5 years of tax returns for both the tax payer and the tax preparer and calculating an average as part of the scoring process, which may incur significant data processing overhead, risk scoring engine need only look up the predetermined and integrated real-time data values, as discussed and illustrated herein, and use them to calculate the risk score. 
     While certain features and embodiments of the disclosure have been described, other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments of the disclosure disclosed herein. Furthermore, although aspects of embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, one skilled in the art will appreciate that these aspects can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or a CD-ROM, or other forms of RAM or ROM. Further, the steps of the disclosed methods may be modified in various ways, including by reordering steps and/or inserting or deleting steps, without departing from the principles of the disclosure. 
     Moreover, one of ordinary skill would appreciate that the disclosure may be used in other fields in addition to tax returns, such as, for example, insurance claims, visa/immigration applications, etc. 
     It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims and their full scope of equivalents.