Patent Publication Number: US-2021182877-A1

Title: Method and system to determine business segments associated with merchants

Description:
BACKGROUND 
     Data management systems, such as transaction data management systems, personal financial management systems, small business accounting and management systems, tax preparation systems, and the like, have proven to be valuable and popular tools for helping users of these systems perform various tasks and manage their personal and professional lives. 
     When the user of a data management system is a merchant, such as a small business owner, it is often necessary to accurately identify the type of commercial activity or “business segment” that is associated with the merchant. Determining the business segment associated with a merchant is often legally mandated in order to meet various reporting and compliance requirements such as capital evaluation, tax reporting, and to prevent illegal operations such money laundering. In addition, determining the business segment associated with a merchant can also be used by the provider of the data management system to provide the user with more relevant information and features. 
     Despite the need to accurately determine the business segment associated with merchant users of data management systems, obtaining this information has historically proven to be difficult. The historic difficulty in accurately determining the business segment associated with merchants has its roots in the fact that, historically, the merchant users themselves have been asked to provide the information regarding the business segment in which they operate. This has proven extremely ineffective with more than 60% of merchants failing to provide accurate data indicating their business segment. In many cases the merchants simply fail to provide any information regarding their business segment. In other cases, the merchants provide incorrect information, either unintentionally or, in some cases, intentionally. 
     One of the reasons so many merchants fail to provide accurate data indicating their business segment is that many merchants do not understand coding systems and specific codes used to identify business segments. Typically, a merchant&#39;s business segment is identified using one or more standardized business segment categories and codes provided through one or more standardized business segment classification systems. Specific examples of standardized business segment classification systems include, but are not limited to, the North American Industry Classification System (NAICS) and the Merchant Category Code system (MCC). However, the categories, classifications, and codes provided through standardized business segment classification systems are often complicated, hierarchically related, and can be quite granular. This makes it difficult for merchants to understand and use these systems and codes. In addition, the codes used by one system, such as NAICS, are entirely different from the codes used by another system, such as MCC. This again makes it difficult for a given merchant to determine what code, or codes, apply to their business activities. 
     In addition, merchants often fail to provide accurate data indicating their business segment because they anticipate changes in their business segment and are hesitant to “lock” themselves into a given segment. For instance, an automobile service provider may envision moving into the auto parts or auto sales business and therefore may be hesitant to identify their business using an automobile service-related code. Similarly, a retail supplier of goods may envision moving into the wholesale market and therefore may identify the business as wholesale when, in fact, presently, the business is retail. 
     In addition, as discussed in more detail below, in some cases such as those involving fraudulent or criminal activity, users may intentionally fail to provide data indicating their business segment or intentionally provide incorrect/inaccurate data indicating their business segment 
     For these, and numerous other reasons, the fact remains that the majority of merchant users of small business data management systems either fail to provide data indicating their business segment or provide incorrect/inaccurate data indicating their business segment. Given the various legally mandated reporting requirements, the desire to provide relevant user experiences, and the desire to identify and prevent fraudulent/illegal activity, this is a significant and long-standing problem for providers of data management systems. 
     What is needed is a technical solution to the technical problem of accurately determining the business segment associated with a merchant user of a data management system. 
     SUMMARY 
     The systems and methods of the present disclosure provide a technical solution to the technical problem of automatically, accurately, effectively, and efficiently determining the business segment associated with a merchant user of a data management system. In addition, the systems and methods of the present disclosure can be used to identify fraudulent or other criminal activity such as fraudulent merchants, criminal monetary transactions, and fake invoices. 
     The systems and methods of the present disclosure provide this technical solution by obtaining categorized merchant financial documents data representing one or more financial documents associated with one or more categorized merchants. Herein, a categorized merchant is a merchant having been identified as conducting business in a respective business segment. 
     The obtained categorized merchant financial documents data is then processed to generate categorized merchant financial document training data by correlating features of the categorized merchant financial documents data for each of the categorized merchants with the respective business segment associated with each of the categorized merchants. 
     The categorized merchant financial document training data is then used to train a machine learning-based merchant business segment prediction model to determine business segment probability scores based on merchant financial document data. 
     Once the machine learning-based merchant business segment prediction model is trained, uncategorized merchant financial document data representing financial documents associated with an uncategorized merchant is obtained. Herein, an uncategorized merchant is a merchant not having been identified as conducting business in a respective business segment. 
     The uncategorized merchant financial document data is then provided to the trained machine learning-based merchant business segment prediction model and a probable business segment for the uncategorized merchant is determined using the machine learning-based merchant business segment prediction model. 
     The determined probable business segment for the uncategorized merchant is then assigned to the previously uncategorized merchant. In one embodiment, probability data indicating the probability the business segment assigned to the merchant is the correct business segment is also provided. Then based in part on the determined probable business segment for the merchant various legal reporting requirements associated with the determined probable business segment for the merchant are met, more relevant user experiences associated with the determined probable business segment for the merchant can be provided; and fraudulent/illegal activity can be more readily identified. 
     Therefore, the systems and methods of the present disclosure use machine learning techniques to automatically and accurately determine the business segment associated with a merchant user of a data management system. Unlike traditional systems which rely on self-reported business segment identification, using the systems and methods of the present disclosure, the business segment is identified using machine learning-based analysis of the actual financial documents generated by, and associated with, the merchant. Consequently, the systems and methods of the present disclosure provide a technical solution to the technical problem of automatically, accurately, effectively, and efficiently determining the business segment associated with a merchant user of a data management system. 
     In addition, in one embodiment, once the one or more merchant business segment prediction models are trained, the systems and methods of the present disclosure are used to identify fraudulent or criminal activity such as fraudulent merchants, criminal monetary transactions, and fake invoices. 
     This is accomplished by obtaining subject merchant financial document data representing financial documents associated with a subject merchant, the subject merchant having been previously identified as conducting business in a respective business segment. The subject merchant financial document data is then provided to the trained machine learning-based merchant business segment prediction model. Using the machine learning-based merchant business segment prediction model, a probable business segment for the subject merchant is determined. The determined probable business segment for the subject merchant is then compared to the previously identified business segment for the subject merchant. If the determined probable business segment for the subject merchant and the previously identified business segment for the subject merchant differ by a threshold level, the subject merchant is labeled for further investigation to determine if fraudulent or criminal activity is present. 
     The systems and methods of the present disclosure use machine learning techniques to automatically and accurately determine the business segment associated with a merchant user of a data management system. In one embodiment, this information to then further utilized to identify potentially fraudulent or criminal activity. As a result, the systems and methods of the present disclosure can be used to: meet various legal reporting requirements; provide more relevant user experience; and more readily identify fraudulent/illegal activity. Consequently, the systems and methods of the present disclosure provide a technical solution to the long-standing technical problem of automatically, accurately, effectively, and efficiently identifying potentially fraudulent activity. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level block diagram of a model training environment for training a machine learning-based merchant business segment prediction model in accordance with one embodiment. 
         FIG. 2  is a high-level block diagram of a runtime environment for implementing a method and system for business segment determination in accordance with one embodiment. 
         FIG. 3  is a high-level block diagram of a runtime environment for implementing a method and system for business segment determination and fraud detection in accordance with one embodiment. 
         FIG. 4  is a flow chart representing a process for training a machine learning-based merchant business segment prediction model in accordance with one embodiment. 
         FIG. 5  is a flow chart representing a process for business segment determination in accordance with one embodiment. 
         FIG. 6  is a flow chart representing a process for business segment determination and fraud detection in accordance with one embodiment. 
     
    
    
     Common reference numerals are used throughout the FIGs. and the detailed description to indicate like elements. One skilled in the art will readily recognize that the above FIGs. are merely illustrative examples and that other architectures, modes of operation, orders of operation, and elements/functions can be provided and implemented without departing from the characteristics and features of the invention, as set forth in the claims. 
     DETAILED DESCRIPTION 
     Embodiments will now be discussed with reference to the accompanying FIGs. which depict one or more exemplary embodiments. Embodiments may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein, shown in the FIGs., and/or described below. Rather, these exemplary embodiments are provided to allow a complete disclosure that conveys the principles of the invention, as set forth in the claims, to those of skill in the art. 
     In accordance with the systems and methods of the present disclosure financial documents associated with categorized merchants who have previously been identified as merchants associated with specific business segments and business segment codes are collected and processed. This data is then used as training data for one or more merchant business segment prediction models using machine learning techniques. 
     Once the one or more merchant business segment prediction models are trained, current and historical financial documents associated with an uncategorized merchant are then collected and processed to generate uncategorized merchant financial document data. The uncategorized merchant financial document data is then provided to the trained one or more merchant business segment prediction models. The trained one or more merchant business segment prediction models then generate data indicating the probability that the uncategorized merchant is associated with one or more specific business segments and/or business segment codes. The specific business segment and/or business segment code determined to be most probably associated with the uncategorized merchant is then assigned to the previously uncategorized merchant. This assigned business segment and/or business segment code is then used to comply with various reporting requirements, provide the merchants with a customized user experience, and to detect fraudulent or other illegal activity. 
     In addition, in one embodiment, once the one or more merchant business segment prediction models are trained, the systems and methods of the present disclosure are used to identify fraudulent or criminal activity such as fraudulent merchants, criminal monetary transactions, and fake invoices. This is accomplished by collecting current and historical financial documents associated with a self-categorized, or previously categorized, “subject” merchant who has previously been associated with a specific business segment or code. The previously categorized merchant financial documents are then processed and provided to the trained one or more merchant business segment prediction models. The trained one or more merchant business segment prediction models then determine a specific business segment and/or business segment code most probably associated with the previously categorized subject merchant. This information is then compared with the previous business segment or code assigned to the previously categorized subject merchant. If the specific business segment and/or business segment code predicted by the one or more merchant business segment prediction models is not the same as the previous business segment or code of the previously categorized subject merchant, or is determined to be too different or inconsistent, then the previously categorized subject merchant is flagged and/or subjected to further analysis or investigation. 
     Consequently, the systems and methods of the present disclosure provide a technical solution to the technical problem of automatically, accurately, effectively, and efficiently determining the business segment associated with a merchant user of a data management system. In addition, the systems and methods of the present disclosure can be used to identify fraudulent activity such as fraudulent merchants, criminal monetary transactions, and fraudulent invoices. 
       FIG. 1  is a high-level block diagram of a model training environment  101  for training a trained machine learning-based merchant business segment prediction model  171 . 
     As seen in  FIG. 1 , model training environment  101  includes merchant financial documents database  112 , merchant financial document data processing module  121 , merchant financial document feature extraction module  122 , model training module  170 , and trained machine learning-based merchant business segment prediction model  171 . 
     As seen in  FIG. 1 , merchant financial documents database  112  includes categorized merchant financial documents data  113  representing financial documents associated with categorized merchants who have previously been identified as merchants associated with specific business segments and business segment codes. 
     Categorized merchant financial documents data  113  typically includes data representing multiple individual documents such as, but not limited to, invoices generated by the categorized merchants; invoices received by the categorized merchants; estimates provided by the categorized merchants; inventory documents associated with the categorized merchants; revenue documents associated with the categorized merchants; accounting documents associated with the categorized merchants; correspondence documents associated with the categorized merchants; social media postings associated with the categorized merchants; website postings associated with the categorized merchants; domain names associated with the categorized merchants; email addresses associated with the categorized merchants; phone numbers associated with the categorized merchants; addresses associated with the categorized merchants; and any other document or business related document data associated with a merchant as discussed herein, known in the art at the time of filing, or as becomes known after the time of filing. 
     As seen in  FIG. 1 , merchant financial documents database  112  also includes uncategorized merchant financial documents data  115  representing financial documents associated with uncategorized merchants who have not previously been identified as merchants associated with specific business segments and business segment codes. 
     Like categorized merchant financial documents data  113 , uncategorized merchant financial documents data  115  can include data representing numerous individual documents such as, but not limited to, invoices generated by the uncategorized merchants; invoices received by the uncategorized merchants; estimates provided by the uncategorized merchants; inventory documents associated with the uncategorized merchants; revenue documents associated with the uncategorized merchants; accounting documents associated with the uncategorized merchants; correspondence documents associated with the uncategorized merchants; social media postings associated with the uncategorized merchants; website postings associated with the uncategorized merchants; domain names associated with the uncategorized merchants; email addresses associated with the uncategorized merchants; phone numbers associated with the uncategorized merchants; addresses associated with the uncategorized merchants; and any other document or business related data associated with a merchant as discussed herein, known in the art at the time of filing, or as becomes known after the time of filing. 
     Categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  can be obtained from multiple sources including, but not limited to, one or more data management systems associated with model training environment  101 . Many data management systems, including, but not limited to, small business data management systems, personal financial data management systems, transaction data management systems, and the like, offer various financial document preparation and submission capabilities such as billing, bill payment, estimates, inventory, and other financial document creation and dissemination capabilities, to the users of these data management systems. Consequently, in one example, at least part of categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  is obtained by collecting various financial documents generated by, submitted to, or processed through, one or more data management systems by merchant users of the data management systems. 
     In some cases, categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  are generated outside of the data management system and are either submitted by a merchant user of the data management system or are uploaded by a customer or other user of the data management system. 
     In some cases, categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  are obtained from data processed and generated by machine learning-based merchant business segment prediction models, such as trained machine learning-based merchant business segment prediction model  171 . 
     In some cases categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  come from any or all sources of categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     As seen in  FIG. 1 , categorized merchant financial documents data  113  is provided to merchant financial document data processing module  121 . At merchant financial document data processing module  121  one or more methods are used to identify and extract categorized merchant business segment data  123 . 
     In various embodiments, extracted categorized merchant business segment data  123  includes data indicating the business segment associated with the categorized merchants of categorized merchant financial documents data  113 . In various embodiments, categorized merchant business segment data  123  represents a business code associated with the categorized merchants of categorized merchant financial documents data  113  such as a North American Industry Classification System (NAICS) code, a Merchant Category Code system (MCC) code, or any code used with any standardized business segment classification systems as discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     As seen in  FIG. 1 , merchant financial document data processing module  121  includes merchant financial document feature extraction module  122 . Merchant financial document feature extraction module  122  is used to identify, extract, and collect categorized merchant financial document feature data  124 . In various embodiments, categorized merchant financial document feature data  124  includes textual and non-textual features in categorized merchant financial documents data  113  such as words, phrases, symbols, numbers etc. 
     The merchant financial document features identified and extracted by merchant financial document feature extraction module  122  can be pre-defined, or pre-identified, as features, or data elements, associated with merchant financial documents that, depending on the present, absence, or state, of the features can be indicative of a business segment associated with each financial document. In some cases, the merchant financial document features are defined by analysis of historically known merchant financial documents and business segments and the elements of those financial documents that were found to be indicative, or not indicative, of the specific business segment. In some cases, the merchant financial document features are defined by analysis performed by human analysts. In other cases, the merchant financial document features are defined and identified by virtue of the processing of categorized merchant financial documents data  113  by one or more processing modules including, but not limited to, one or more machine learning-based models. In some cases, the merchant financial document features are defined and identified by machine learning-based merchant business segment prediction models, such as trained machine learning-based merchant business segment prediction model  171 . 
     In one example, Optical Character Recognition (OCR) techniques are used by merchant financial document feature extraction module  122  to identify and extract the categorized merchant financial document feature data  124  and categorized merchant business segment data  123  associated with each of the financial documents included in the categorized merchant financial documents data  113 . Various OCR systems and techniques are well known to those of skill in the art. Consequently, a more detailed description of the operation of any specific OCR technique used to identify and extract categorized merchant financial document feature data  124  and categorized merchant business segment data  123  associated with each of the financial documents included in categorized merchant financial documents data  113  is omitted here to avoid detracting from the invention. 
     Returning to  FIG. 1 , in order for merchant financial document feature extraction module  122  to identify the features present in a given invoice of categorized merchant financial documents data  113  it is important that categorized merchant financial document feature data  124  and categorized merchant business segment data  123  be processed by one or more methods to indicate not only that the merchant financial document feature is present, but also the location of the merchant financial document feature data in the merchant financial document data. In one example, this is accomplished by using a combination of OCR techniques discussed above and JavaScript Object Notation (JSON). 
     JSON is an open-standard file format that uses human readable text to transmit data objects consisting of attribute-value pairs and array data types. Importantly, when text is converted into JSON file format each object in the text is described as an object at a very precise location in the text document. Consequently, when text data, such as categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115 , is converted into JSON file format, the name of the potential merchant financial document feature is indicated as the object and the precise location of the object and data associated with that object in the vicinity of the object is indicated. Consequently, by converting categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  into a JSON file format, the identification of the merchant financial document features within the merchant financial document data is a relatively trivial task. JSON is well known to those of skill in the art, therefore a more detailed discussion of JSON, and JSON file formatting, is omitted here to avoid detracting from the invention. 
     Once the merchant financial document features are identified and extracted as merchant financial document feature data for each financial document represented in categorized merchant financial documents data  113  by merchant financial document feature extraction module  122 , the merchant financial document feature data for all of the financial documents represented in categorized merchant financial documents data  113  is collected as categorized merchant financial document feature data  124 . 
     As seen in  FIG. 1 , once categorized merchant financial document feature data  124  and categorized merchant business segment data  123  is generated, categorized merchant financial document feature data  124  and categorized merchant business segment data  123  are correlated to generate categorized merchant financial documents training data  130 . Categorized merchant financial documents training data  130  can include categorized merchant financial document feature data  124  and categorized merchant business segment data  123  arranged in a machine learning-based merchant business segment prediction model training data matrix and used as training data to train a supervised machine learning-based merchant business segment prediction model. In this case, rows of feature data from categorized merchant financial document feature data  124  represent categorized merchant financial document feature vector data associated with each categorized merchant financial document and are used as input objects by model training module  170  to train a machine learning-based merchant business segment prediction model. In these supervised learning examples, categorized merchant business segment data  123  are arranged as entries in a label column and are used as supervisory signals, or labels. 
     Categorized merchant financial documents training data  130  is then provided to model training module  170  where it is used as training data to generate trained machine learning-based merchant business segment prediction model  171 . In this case, the rows of categorized merchant financial document feature data  124  represent categorized merchant document feature vector data associated with each categorized merchant document and are used as input objects by model training module  170  to train a machine learning-based merchant business segment prediction model. In these supervised learning examples, the data entries from categorized merchant business segment data  123  are arranged in a label column and are used as supervisory signals, or labels. 
     Those of skill in the art will recognize that, in practice, categorized merchant financial documents training data  130  may include, hundreds, thousands, or millions of rows representing hundreds, thousands, or millions of known merchant business segments and that more rows can be added representing more business segments as those business segments are identified and associated with categorized merchant document features. 
     As discussed in more detail below, once trained machine learning-based merchant business segment prediction model  171  is generated, trained machine learning-based merchant business segment prediction model  171  is deployed in a runtime environment, such as runtime environment  201  of  FIG. 2  or runtime environment  301  of  FIG. 3 . As also discussed below, once implemented in a runtime environment, trained machine learning-based merchant business segment prediction model  171  is used to generate probable business segment data for merchants based on merchant financial document data associated with the merchants. 
       FIG. 2  is a high-level block diagram of a runtime environment  201  for implementing a method and system for business segment determination in accordance with one embodiment. 
     As seen in  FIG. 2 , runtime environment  201  includes merchant financial documents database  112 , merchant financial document data processing module  121 , merchant financial document feature extraction module  122 , trained machine learning-based merchant business segment prediction model  171 , business segment determination module  225 , and business segment assignment module  260 . 
     As seen in  FIG. 2 , merchant financial documents database  112  includes uncategorized merchant financial documents data  115  representing financial documents associated with uncategorized merchants who have not previously been identified as merchants associated with specific business segments and business segment codes. 
     As discussed above, uncategorized merchant financial documents data  115  can include data representing numerous individual documents such as, but not limited to, invoices generated by the uncategorized merchants; invoices received by the uncategorized merchants; estimates provided by the uncategorized merchants; inventory documents associated with the uncategorized merchants; revenue documents associated with the uncategorized merchants; accounting documents associated with the uncategorized merchants; correspondence documents associated with the uncategorized merchants; social media postings associated with the uncategorized merchants; website postings associated with the uncategorized merchants; domain names associated with the uncategorized merchants; email addresses associated with the uncategorized merchants; phone numbers associated with the uncategorized merchants; addresses associated with the uncategorized merchants; and any other document or business related data associated with a merchant as discussed herein, known in the art at the time of filing, or as becomes known after the time of filing. 
     As discussed above, uncategorized merchant financial documents data  115  can be obtained from multiple sources including, but not limited to, one or more data management systems associated with runtime environment  201 . Consequently, in one example, at least part of uncategorized merchant financial documents data  115  is obtained by collecting various financial documents generated by, submitted to, or processed through, one or more data management systems by merchant users of the data management systems. 
     In some cases, uncategorized merchant financial documents data  115  is generated outside of the data management system and is either submitted by a merchant user of the data management system or is uploaded by a customer or other user of the data management system. 
     In some cases, uncategorized merchant financial documents data  115  is obtained from data processed and generated by machine learning-based merchant business segment prediction models, such as trained machine learning-based merchant business segment prediction model  171 . 
     In some cases uncategorized merchant financial documents data  115  comes from any or all sources of categorized merchant financial documents data  113  and uncategorized merchant financial documents data  115  discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     As seen in  FIG. 2 , uncategorized merchant financial documents data  115  is provided to merchant financial document data processing module  121 . As discussed above, merchant financial document data processing module  121  includes merchant financial document feature extraction module  122 . Merchant financial document feature extraction module  122  is used to identify, extract, and collect uncategorized merchant financial document feature data  224 . In various embodiments, uncategorized merchant financial document feature data  224  includes textual and non-textual features in uncategorized merchant financial documents data  115  such as words, phrases, symbols, numbers etc. 
     As discussed above, the merchant financial document features identified and extracted by merchant financial document feature extraction module  122  can be pre-defined, or pre-identified, as features, or data elements, associated with merchant financial documents that, depending on the present, absence, or state, of the features can be indicative of a business segment associated with each financial document. In some cases, the merchant financial document features are defined by analysis of historically known merchant financial documents and business segments and the elements of those financial documents that were found to be indicative, or not indicative, of the specific business segment. In some cases, the merchant financial document features are defined by analysis performed by human analysts. In other cases, the merchant financial document features are defined and identified by virtue of the processing of uncategorized merchant financial documents data  115  by one or more processing modules including, but not limited to, one or more machine learning-based models. In some cases, the merchant financial document features are defined and identified by machine learning-based merchant business segment prediction models, such as trained machine learning-based merchant business segment prediction model  171 . 
     As noted above, in one example, Optical Character Recognition (OCR) techniques and/or JSON formatting are used by merchant financial document feature extraction module  122  to identify and extract the uncategorized merchant financial document feature data  224  associated with each of the financial documents included in the uncategorized merchant financial documents data  115 . Various OCR systems and techniques are well known to those of skill in the art. 
     Once the uncategorized merchant financial document features are identified and extracted as uncategorized merchant financial document feature data for each financial document represented in uncategorized merchant financial documents data  115  by merchant financial document feature extraction module  122 , the uncategorized merchant financial document feature data for all of the financial documents represented in uncategorized merchant financial documents data  115  is collected as uncategorized merchant financial document feature data  224 . 
     As seen in  FIG. 1 , once uncategorized merchant financial document feature data  224  is generated, uncategorized merchant financial document feature data  224  is provided to trained machine learning-based merchant business segment prediction model  171 . Trained machine learning-based merchant business segment prediction model  171  can be a machine learning-based merchant business segment prediction model trained as described above with respect to  FIG. 1  and the description of model training environment  101 . 
     Once uncategorized merchant financial document feature data  224  is provided to trained machine learning-based merchant business segment prediction model  171 , trained machine learning-based merchant business segment prediction model  171  generates probable business segment for the uncategorized merchant data  230 . Probable business segment for the uncategorized merchant data  230  includes data indicating one or more business segments associated with the uncategorized merchant. 
     In various embodiments, probable business segment for the uncategorized merchant data  230  represents one or more business codes determined to be associated with the uncategorized merchant of uncategorized merchant financial documents data  115  such as a North American Industry Classification System (NAICS) code, a Merchant Category Code system (MCC) code, or any code used with any standardized business segment classification systems as discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     Probable business segment for the uncategorized merchant data  230  can also include business segment probability data  231  indicating the probability that the uncategorized merchant is associated with each specific business segment and/or business segment code indicated in probable business segment for the uncategorized merchant data  230 . In various embodiments, business segment probability data  231  can represent a business segment probability score for each specific business segment and/or business segment code indicated in probable business segment for the uncategorized merchant data  230 . 
     When probable business segment for the uncategorized merchant data  230  includes business segment probability data  231 , the value or score indicated by business segment probability data  231  is compared at threshold compare module  250  to a predetermined threshold business segment probability represented by threshold business segment probability data  240 . 
     If a business segment probability or probability score for a specific business segment represented by business segment probability data  231  is greater than a threshold business segment probability or probability score represented by threshold business segment probability data  240 , then the specific business segment is assigned to the previously uncategorized merchant at business segment assignment module  260 . 
     Once a specific business segment is assigned to the previously uncategorized merchant at business segment assignment module  260 , then the business segment determined and assigned to the previously uncategorized merchant is used to dictate various actions to be performed with respect to the now newly categorized merchant. These actions can include, but are not limited to, ensuring legal reporting requirements associated with the business segment determined and assigned to the previously uncategorized merchant are met; customizing a data management system user experience provided to the previously uncategorized merchant based on the business segment determined and assigned to the previously uncategorized merchant, and, as discussed in more detail below, to identify and prevent fraudulent/illegal activity. 
     As noted above, the methods and systems disclosed herein can be used to identify fraudulent or criminal activity such as fraudulent merchants, criminal monetary transactions, and fake invoices. 
     As one example of using the methods and systems disclosed herein to identify fraudulent or criminal activity, once the one or more merchant business segment prediction models are trained, the systems and methods of the present disclosure can be used to identify fraudulent or criminal activity by obtaining a current or historical financial document associated with a self-categorized merchant who has previously provided a specific business segment or code. The self-categorized merchant financial document is then processed to generate self-categorized merchant financial document data. The self-categorized merchant financial document data is then provided to the trained one or more merchant business segment prediction models. The trained one or more merchant business segment prediction models then generate data indicating the probability that the self-categorized merchant financial document is associated with a specific business segment and/or business segment code. This data is then compared with the self-categorization data provided by the self-categorized merchant. If the specific business segment and/or business segment code predicted by the one or more merchant business segment prediction models to be associated with the merchant financial document data is not the same as the self-categorization data provided by the self-categorized merchant, or is determined to be too different or inconsistent, then the self-categorized merchant is flagged and/or subjected to further analysis or investigation. 
     As another example of using the methods and systems disclosed herein to identify fraudulent or criminal activity, once the one or more merchant business segment prediction models are trained, the systems and methods of the present disclosure are used to identify fraudulent or criminal activity by collecting a current or historical financial document associated with a categorized merchant who has previously been assigned or has provided a specific business segment or code. The categorized merchant financial document is then processed to generate categorized merchant financial document data. The categorized merchant financial document data is then provided to the trained one or more merchant business segment prediction models. The trained one or more merchant business segment prediction models then generate data indicating the probability that the categorized merchant financial document is associated with a specific business segment and/or business segment code. This data is then compared with the categorization data currently associated with the categorized merchant. If the specific business segment and/or business segment code predicted by the one or more merchant business segment prediction models to be associated with the merchant financial document data is not the same as the current categorization data for the categorized merchant, or is determined to be too different or inconsistent, then the categorized merchant is flagged and/or subjected to further analysis or investigation. 
     In one embodiment, once the one or more merchant business segment prediction models are trained, the systems and methods of the present disclosure are used to identify fraudulent or criminal activity by collecting current and historical financial documents associated with a subject merchant who can be a previously categorized merchant, such as a self-categorized merchant, who has previously been assigned a specific business segment or code. The subject merchant financial documents are then processed to generate subject merchant financial document data. The subject merchant financial document data is then provided to the trained one or more merchant business segment prediction models. The trained one or more merchant business segment prediction models then generate data indicating the probability that the subject merchant is associated with a specific business segment and/or business segment code. This data is then compared with the previously assigned or self-provided categorization data. If the specific business segment and/or business segment code predicted by the one or more merchant business segment prediction models is not the same as the previously assigned or self-provided business segment, or is determined to be too different or inconsistent, then the subject merchant is flagged and/or subjected to further analysis or investigation. 
       FIG. 3  is a high-level block diagram of a runtime environment for implementing a method and system for business segment determination and fraud detection in accordance with one embodiment. 
     As seen in  FIG. 3 , runtime environment  301  includes merchant financial documents database  112 , merchant financial document data processing module  121 , merchant financial document feature extraction module  122 , trained machine learning-based merchant business segment prediction model  171 , business segment determination module  325 , business segment compare module  370 , and protective action module  380 . 
     As seen in  FIG. 3 , merchant financial documents database  112  includes subject merchant data  313 . The subject merchant of  FIG. 3  can be a merchant being analyzed to confirm the subject merchant is associated with the correct business segment. In various embodiments, the subject merchant may be selected for analysis based on random selection, periodic review, or any indication that the subject merchant may not be associated with the correct business segment. 
     Subject merchant data  313  can include subject merchant financial documents data  315  representing financial documents associated with the subject merchant and previously assigned subject merchant categorization data  317  representing the previously assigned/reported business segment associated with the subject merchant. 
     In some cases, the previously assigned/reported business segment associated with the subject merchant represented by previously assigned subject merchant categorization data  317  may have been self-reported by the subject merchant. In some cases, the previously assigned/reported business segment associated with the subject merchant represented by subject merchant categorization data  317  may have been assigned to the subject merchant. 
     The previously assigned/reported business segment associated with the subject merchant represented by previously assigned subject merchant categorization data  317  can be in the form of a business segment code such as a North American Industry Classification System (NAICS) code, a Merchant Category Code system (MCC) code, or any code used with any standardized business segment classification systems as discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     Subject merchant financial documents data  315  can include data representing numerous individual documents such as, but not limited to, invoices generated by the subject merchant; invoices received by the subject merchant; estimates provided by the subject merchant; inventory documents associated with the subject merchant; revenue documents associated with the subject merchant; accounting documents associated with the subject merchant; correspondence documents associated with the subject merchant; social media postings associated with the subject merchant; website postings associated with the subject merchant; domain names associated with the subject merchant; email addresses associated with the subject merchant; phone numbers associated with the subject merchant; addresses associated with the subject merchant; and any other document or business related data associated with a merchant as discussed herein, known in the art at the time of filing, or as becomes known after the time of filing. 
     Subject merchant financial documents data  315  can be obtained from multiple sources including, but not limited to, one or more data management systems associated with runtime environment  301 . Consequently, in one example, at least part of subject merchant financial documents data  315  is obtained by collecting various financial documents generated by, submitted to, or processed through, data management systems by subject merchant users of the data management systems. 
     In some cases, subject merchant financial documents data  315  is generated outside of the data management system and is either submitted by a subject merchant user of the data management system or is uploaded by a customer or other user of the data management system. 
     In some cases, subject merchant financial documents data  315  comes from any or all sources of subject merchant financial documents data  315  discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     As seen in  FIG. 3 , subject merchant financial documents data  315  is provided to merchant financial document data processing module  121 . As discussed above, merchant financial document data processing module  121  includes merchant financial document feature extraction module  122 . Merchant financial document feature extraction module  122  is used to identify, extract, and collect subject merchant financial document feature data  324 . In various embodiments, subject merchant financial document feature data  324  includes textual and non-textual features in subject merchant financial documents data  315  such as words, phrases, symbols, numbers etc. 
     As discussed above, the merchant financial document features identified and extracted by merchant financial document feature extraction module  122  can be pre-defined, or pre-identified, as features, or data elements, associated with merchant financial documents that, depending on the present, absence, or state, of the features can be indicative of a business segment associated with each financial document. In some cases, the merchant financial document features are defined by analysis of historically known merchant financial documents and business segments and the elements of those financial documents that were found to be indicative, or not indicative, of the specific business segment. In some cases, the merchant financial document features are defined by analysis performed by human analysts. In other cases, the merchant financial document features are defined and identified by virtue of the processing of subject merchant financial documents data  315  by one or more processing modules including, but not limited to, one or more machine learning-based models. In some cases, the merchant financial document features are defined and identified by machine learning-based merchant business segment prediction models, such as trained machine learning-based merchant business segment prediction model  171 . 
     As noted above, in one example, Optical Character Recognition (OCR) techniques and/or JSON formatting are used by merchant financial document feature extraction module  122  to identify and extract the subject merchant financial document feature data  324  associated with each of the financial documents included in the subject merchant financial documents data  315 . Various OCR systems and techniques are well known to those of skill in the art. 
     Once the subject merchant financial document features are identified and extracted as subject merchant financial document feature data for each financial document represented in subject merchant financial documents data  315  by merchant financial document feature extraction module  122 , the subject merchant financial document feature data for all of the financial documents represented in subject merchant financial documents data  315  is collected as subject merchant financial document feature data  324 . 
     As seen in  FIG. 3 , once subject merchant financial document feature data  324  is generated, subject merchant financial document feature data  324  is provided to trained machine learning-based merchant business segment prediction model  171 . Trained machine learning-based merchant business segment prediction model  171  can be a machine learning-based merchant business segment prediction model trained as described above with respect to  FIG. 1  and the description of model training environment  101 . 
     Once subject merchant financial document feature data  324  is provided to trained machine learning-based merchant business segment prediction model  171 , trained machine learning-based merchant business segment prediction model  171  generates probable business segment for the subject merchant data  330 . Probable business segment for the subject merchant data  330  includes data indicating one or more business segments associated with the subject merchant. 
     In various embodiments, probable business segment for the subject merchant data  330  represents one or more business codes determined to be associated with the uncategorized merchant of subject merchant financial documents data  315  such as a North American Industry Classification System (NAICS) code, a Merchant Category Code system (MCC) code, or any code used with any standardized business segment classification systems as discussed herein, or known in the art at the time of filing, or as become known after the time of filing. 
     Probable business segment for the subject merchant data  330  can also include business segment probability data  331  indicating the probability that the subject merchant is associated with each specific business segment and/or business segment code indicated in probable business segment for the subject merchant data  330 . In various embodiments, business segment probability data  331  can represent a business segment probability score for each specific business segment and/or business segment code indicated in probable business segment for the subject merchant data  330 . 
     When probable business segment for the subject merchant data  330  includes business segment probability data  331 , the value or score indicated by business segment probability data  331  is compared at threshold compare module  350  to a predetermined threshold business segment probability represented by threshold business segment probability data  340 . 
     If a business segment probability or probability score for a specific business segment represented by business segment probability data  331  is greater than a threshold business segment probability or probability score represented by threshold business segment probability data  340 , then determined business segment data  360  is generated representing that specific business segment. 
     Once determined business segment data  360  is generated for the subject merchant, determined business segment data  360  and previously assigned subject merchant categorization data  317  are provided to business segment compare module  370 . 
     At business segment compare module  370  the determined business segment represented by determined business segment data  360  is compared to the previously assigned business segment represented by previously assigned subject merchant categorization data  317 . If the determined business segment represented by determined business segment data  360  differs from the previously assigned business segment represented by previously assigned subject merchant categorization data  317  by a threshold amount/level, then one or more protective actions are taken at protective action module  380  to identify and prevent fraudulent or other criminal activity. 
     The one or more protective actions that can be taken by protective action module  380  include, but are not limited to, contacting the subject merchant to clarify the discrepancy in business segment assignment; assigning the newly determined business segment to the subject merchant; suspending all subject merchant activity within a data management system used by the subject merchant until the discrepancy in business segment assignment is resolved; sending financial document data associated with the subject merchant to a fraud/criminal activity specialist for analysis; closing down any accounts within a data management system used by the subject merchant; or any other protective action as discussed herein, or known at the time of filing, or that become known after the time of filing. 
       FIG. 4  is a flow chart representing a process  400  for training a machine learning-based merchant business segment prediction model in accordance with one embodiment. 
     Referring to  FIGS. 1 and 4  together, process  400  begins at operation  401  and process flow proceeds to operation  403 . 
     At operation  403  one or more financial documents associated with one or more categorized merchants, such as any of the financial documents discussed above with respect to  FIG. 1 , are obtained using any of the sources or methods discussed above with respect to  FIG. 1 . 
     Once one or more financial documents associated with one or more categorized merchants are obtained at operation  403 , process flow proceeds to operation  405 . 
     At operation  405 , the financial documents associated with one or more categorized merchants are processed by any of the methods discussed above with respect to  FIG. 1  to generate categorized merchant financial document training data such as any of the categorized merchant financial document training data discussed above with respect to  FIG. 1 . 
     Once categorized merchant financial document training data is generated at operation  405 , process flow proceeds to operation  407 . 
     At operation  407 , the categorized merchant financial document training data is used to train a machine learning-based merchant business segment prediction model used to generate probable business segment data for merchants based on merchant financial document data associated with the merchants using any of the methods discussed above with respect to  FIG. 1 . 
     Once a machine learning-based merchant business segment prediction model is trained to generate probable business segment data for merchants based on merchant financial document data associated with the merchants at operation  407 , process flow proceeds to end operation  430 . At end operation  430 , process  400  is exited to await new data. 
       FIG. 5  is a flow chart representing a process  500  for business segment determination in accordance with one embodiment. 
     Referring to  FIGS. 1, 2 and 5  together, process  500  begins at operation  501  and process flow proceeds to operation  503 . 
     At operation  503  one or more financial documents associated with one or more categorized merchants, such as any of the financial documents discussed above with respect to  FIG. 1 , are obtained using any of the sources or methods discussed above with respect to  FIG. 1 . 
     Once one or more financial documents associated with one or more categorized merchants are obtained at operation  503 , process flow proceeds to operation  505 . 
     At operation  505 , the financial documents associated with one or more categorized merchants are processed by any of the methods discussed above with respect to  FIG. 1  to generate categorized merchant financial document training data such as any of the categorized merchant financial document training data discussed above with respect to  FIG. 1 . 
     Once categorized merchant financial document training data is generated at operation  505 , process flow proceeds to operation  507 . 
     At operation  507 , the categorized merchant financial document training data is used to train a machine learning-based merchant business segment prediction model used to generate probable business segment data for merchants based on merchant financial document data associated with the merchants using any of the methods discussed above with respect to  FIG. 1 . 
     Once a machine learning-based merchant business segment prediction model is trained to generate probable business segment data for merchants based on merchant financial document data associated with the merchants at operation  507 , process flow proceeds to operation  509 . 
     At operation  509  one or more financial documents associated with an uncategorized merchant, such as any of the financial documents discussed above with respect to  FIG. 1  and  FIG. 2 , are obtained using any of the sources or methods discussed above with respect to  FIG. 1  and  FIG. 2 . 
     Once one or more financial documents associated with an uncategorized merchant are obtained at operation  509 , process flow proceeds to operation  511 . 
     At operation  511 , the one or more financial documents associated with an uncategorized merchant of operation  509  are processed to generate uncategorized merchant financial document data using any of the methods discussed above with respect to  FIG. 2 . 
     Once uncategorized merchant financial document data is generated at operation  511 , process flow proceeds to operation  513 . 
     At operation  513 , the uncategorized merchant financial document data of operation  511  is provided to the trained machine learning-based merchant business segment prediction model of operation  507 . 
     Once the uncategorized merchant financial document data is provided to the trained machine learning-based merchant business segment prediction model at operation  513 , process flow proceeds to operation  515 . 
     At operation  515 , the trained machine learning-based merchant business segment prediction model of operation  507  uses the uncategorized merchant financial document data of operation  511  to determine one or more probable business segments for the uncategorized merchant and generate probable business segment data for the uncategorized merchant using any of the methods discussed above with respect to  FIG. 2 . 
     Once probable business segment data is generated for the uncategorized merchant at operation  515 , process flow proceeds to operation  517 . 
     At operation  517 , a business segment is assigned to the uncategorized merchant based, at least in part, on the probably business segment data generated for the uncategorized merchant at operation  515 . 
     Once a business segment is assigned to the uncategorized merchant at operation  517 , process flow proceeds to end operation  530 . At end operation  530 , process  500  is exited to await new data. 
       FIG. 6  is a flow chart representing a process  600  for business segment determination and fraud detection in accordance with one embodiment. 
     Referring to  FIGS. 1, 3 and 6  together, process  600  begins at operation  601  and process flow proceeds to operation  603 . 
     At operation  603  one or more financial documents associated with one or more categorized merchants, such as any of the financial documents discussed above with respect to  FIG. 1 , are obtained using any of the sources or methods discussed above with respect to  FIG. 1 . 
     Once one or more financial documents associated with one or more categorized merchants are obtained at operation  603 , process flow proceeds to operation  605 . 
     At operation  605 , the financial documents associated with one or more categorized merchants are processed by any of the methods discussed above with respect to  FIG. 1  to generate categorized merchant financial document training data such as any of the categorized merchant financial document training data discussed above with respect to  FIG. 1 . 
     Once categorized merchant financial document training data is generated at operation  605 , process flow proceeds to operation  607 . 
     At operation  607 , the categorized merchant financial document training data is used to train a machine learning-based merchant business segment prediction model used to generate probable business segment data for subject merchants based on subject merchant financial document data associated with the subject merchants using any of the methods discussed above with respect to  FIG. 1 . 
     Once a machine learning-based merchant business segment prediction model is trained to generate probable business segment data for subject merchants based on subject merchant financial document data associated with the subject merchants at operation  607 , process flow proceeds to operation  609 . 
     At operation  609  previously assigned subject merchant categorization data, such as any of the previously assigned subject merchant categorization data discussed above with respect to  FIG. 3 , is obtained that represents a business segment previously assigned to a subject merchant. 
     Once previously assigned subject merchant categorization data is obtained at operation  609 , process flow proceeds to operation  611 . 
     At operation  611 , one or more financial documents associated with a subject merchant, such as any of the financial documents discussed above with respect to  FIG. 1  and  FIG. 3 , are obtained using any of the sources or methods discussed above with respect to  FIG. 1  and  FIG. 3 . 
     Once one or more financial documents associated with a subject merchant are obtained at operation  611 , process flow proceeds to operation  613 . 
     At operation  613 , the one or more financial documents associated with the subject merchant of operation  611  are processed to generate subject merchant financial document data using any of the methods discussed above with respect to  FIG. 3 . 
     Once subject merchant financial document data is generated at operation  613 , process flow proceeds to operation  615 . 
     At operation  615 , the subject merchant financial document data of operation  613  is provided to the trained machine learning-based merchant business segment prediction model of operation  607 . 
     Once the subject merchant financial document data is provided to the trained machine learning-based merchant business segment prediction model at operation  615 , process flow proceeds to operation  617 . 
     At operation  617 , the trained machine learning-based merchant business segment prediction model of operation  607  uses the subject merchant financial document data of operation  613  to determine one or more probable business segments for the subject merchant and generate probable business segment data for the subject merchant using any of the methods discussed above with respect to  FIG. 3 . 
     Once probable business segment data is generated for the subject merchant at operation  617 , process flow proceeds to operation  619 . 
     At operation  619 , the determined probable business segment data for the subject merchant of operation  617  is compared to the previously assigned subject merchant categorization data of operation  609  using any of the methods discussed above with respect to  FIG. 3 . 
     Once the determined probable business segment data for the subject merchant is compared to the previously assigned subject merchant categorization data for the subject merchant at operation  619 , process flow proceeds to operation  621 . 
     At operation  621 , if the determined business segment represented by determined probable business segment data for the subject merchant of operation  617  differs from the previously assigned business segment represented by the previously assigned subject merchant categorization data for the subject merchant of operation  609  by a threshold amount/level, then one or more protective actions are taken to identify and prevent fraudulent or other criminal activity. 
     Once, if the determined business segment differs from the previously assigned business segment by a threshold amount/level, one or more protective actions are taken to identify and prevent fraudulent or other criminal activity at operation  621 , process flow proceeds to end operation  630 . At end operation  630 , process  600  is exited to await new data. 
     In the discussion above, certain aspects of one embodiment include process steps and/or operations and/or instructions described herein for illustrative purposes in a specific order and/or grouping. However, the specific order and/or grouping shown and discussed herein are illustrative only and not limiting. Those of skill in the art will recognize that other orders and/or grouping of the process steps and/or operations and/or instructions are possible and, in some embodiments, one or more of the process steps and/or operations and/or instructions discussed above can be combined and/or deleted. In addition, portions of one or more of the process steps and/or operations and/or instructions can be re-grouped as portions of one or more other of the process steps and/or operations and/or instructions discussed herein. Consequently, the specific order and/or grouping of the process steps and/or operations and/or instructions discussed herein do not limit the scope of the invention as claimed below. 
     As discussed in more detail above, using the above embodiments, with little or no modification and/or input, there is considerable flexibility, adaptability, and opportunity for customization to meet the specific needs of various users under numerous circumstances. 
     The present invention has been described in particular detail with respect to specific possible embodiments. Those of skill in the art will appreciate that the invention may be practiced in other embodiments. For example, the nomenclature used for components, capitalization of component designations and terms, the attributes, data structures, or any other programming or structural aspect is not significant, mandatory, or limiting, and the mechanisms that implement the invention or its features can have various different names, formats, or protocols. Further, the system or functionality of the invention may be implemented via various combinations of software and hardware, as described, or entirely in hardware elements. Also, particular divisions of functionality between the various components described herein are merely exemplary, and not mandatory or significant. Consequently, functions performed by a single component may, in other embodiments, be performed by multiple components, and functions performed by multiple components may, in other embodiments, be performed by a single component. 
     Some portions of the above description present the features of the present invention in terms of algorithms and symbolic representations of operations, or algorithm-like representations, of operations on information/data. These algorithmic or algorithm-like descriptions and representations are the means used by those of skill in the art to most effectively and efficiently convey the substance of their work to others of skill in the art. These operations, while described functionally or logically, are understood to be implemented by computer programs or computing systems. Furthermore, it has also proven convenient at times to refer to these arrangements of operations as steps or modules or by functional names, without loss of generality. 
     In addition, the operations shown in the FIGs., or as discussed herein, are identified using a particular nomenclature for ease of description and understanding, but other nomenclature is often used in the art to identify equivalent operations. 
     Therefore, numerous variations, whether explicitly provided for by the specification or implied by the specification or not, may be implemented by one of skill in the art in view of this disclosure.