Title of Invention: AUTOMATED AUDITING AND RECOMMENDATION SYSTEMS AND METHODS Abstract: The present invention relates to systems and methods for automated audit analysis and providing of cost reduction recommendations associated with services used and/or received by an entity. In general, invoice data associated with a service associated with an entity (e.g. a customer) is obtained and processed in order to identify at least one of billing errors and inefficiencies. The processing of invoice data is generally performed by computationally identifying relevant invoice information and comparing this information with data from a proprietary service optimization database. When errors and/or inefficiencies are identified, recommendations to remedy the inefficiencies and/or errors are provided to the entity so that cost reduction actions can be taken. Detailed Description: CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application 63/180,508, filed Apr. 27, 2021, titled “AUTOMATED AUDITING AND RECOMMENDATION SYSTEMS AND METHODS,” which is herein incorporated by reference in its entirety. BACKGROUND For each service a customer receives from a provider, the associated service contract(s) and/or recurring invoices may include various billing errors such as improper fees and taxes, missed incentives, exemptions and/or credits available to a given customer, or simple inefficiencies such as not having the optimal plan for the particular customer's usage or needs. From large companies to small businesses and individual homeowners, a wide range of services may be received such as utility services (e.g. electric, gas, water, etc.), contracted services (e.g. telecom/wireless, information technology hardware/software, waste management, etc.), financial services (e.g. merchant processing, bank fees, retirement plans, insurance, etc.), and real estate services (e.g. property tax assessments, relocation assessments, cost segregation, etc.), depending on the particular customer. For each of these services there may be different potential cost savings available to a given customer. One possible approach to identify potential cost savings associated with service contracts and invoices, is customer driven review of invoices and/or service contracts and comparison with their current needs. However, even in circumstances where a customer felt there was potential to reduce costs, many customers may not know what action to take or where to look for available cost saving opportunities. For example, invoices may be complex, difficult to understand, and a single line entry may represent several charges lumped together, some of which may not be applicable to a given customer. In addition, some customers may not even be aware that opportunities to reduce costs exist. An alternative approach to identify potential cost savings could be to hire an external auditing consultant. In either of these approaches, invoice and service contract auditing is often a manual process which can be time intensive and expensive. Such a time and cost investment for manual audit analysis may only be affordable and beneficial to large customers where significant savings can be recognized. A further complication associated with auditing of service contracts and invoices includes the need for regular and routine auditing which again can be time consuming and costly and additionally requires staying up to date with available programs, exemptions, incentives, rate plans, etc. as fee structures and government regulations can change over time. SUMMARY The systems and methods disclosed herein can vastly simplify invoice and service contract auditing processes for companies of all sizes, as well as provide a simple and affordable auditing solution for individual customers. Rather than a customer needing to identify errors and inefficiencies themselves, they simply provide invoice data (e.g. invoices and/or service contracts), generally in electronic form, to an audit module which in turn performs the necessary audit analysis. The audit module receives the invoice data, automatically extracts information relevant to the auditing process, compares the extracted information with service optimization parameters, and makes recommendations as a result of the comparison in order to optimize service and reduce costs. In one aspect, the extraction process involves determining a service provider, then using an appropriate template for that service provider or service type, if a template is available, in order to more accurately and efficiently extract the relevant information. If a template is not available the systems and methods herein may use extracted information to build templates so that future processing of invoices and/or service contracts of similar service providers/types can be accomplished in a more efficient manner. The extracted data can include items such as service address, account number, usage data, fees, taxes, total invoice amount, and the like which are then compared against the optimization parameters to identify errors and inefficiencies by verifying aspects such as optimal rate structures, service levels, fees, unnecessary charges, availability of programs, benefits, incentives, tax exemptions, and the like. Any identified errors and inefficiencies are compiled in a detailed report along with a recommended plan of action for the customer to recover overages and/or adjust their service in order to reduce future costs. As a result of the reduced time requirements associated with the disclosed auditing processes, the cost of auditing is thereby reduced. Furthermore, on a regular basis customers simply need to provide the repeating invoice data (e.g. monthly invoices), and the audit module automatically updates the audit analysis using the disclosed systems and methods. An additional benefit of the disclosed systems and methods includes the ability to process substantially more invoices and service contracts in significantly less time than typical manual audit approaches. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings illustrate several embodiments and, together with the description, serve to explain the principles of the invention according to the embodiments. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary and are not to be considered as limiting of the scope of the invention or the claims herein in any way. FIG. 1 illustrates an automated auditing and recommendation system in accordance with an exemplary embodiment of the invention. FIG. 2 illustrates an automated auditing and recommendation system in accordance with an exemplary embodiment of the present invention. FIG. 3 illustrates an automated auditing and recommendation process according to one embodiment of the invention. FIG. 4 illustrates one embodiment of the computing architecture that supports an embodiment of the inventive disclosure. FIG. 5 illustrates components of a system architecture that supports an embodiment of the inventive disclosure. FIG. 6 illustrates components of a computing device that supports an embodiment of the inventive disclosure. FIG. 7 illustrates components of a computing device that supports an embodiment of the inventive disclosure. DETAILED DESCRIPTION One or more different embodiments may be described in the present application. Further, for one or more of the embodiments described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the embodiments contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous embodiments, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the embodiments, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the embodiments. Particular features of one or more of the embodiments described herein may be described with reference to one or more particular embodiments or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular embodiments or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the embodiments nor a listing of features of one or more of the embodiments that must be present in all arrangements. Headings of sections provided in this patent application and the title of this patent application are for convenience only and are not to be taken as limiting the disclosure in any way. Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical. A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible embodiments and in order to more fully illustrate one or more embodiments. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the embodiments, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some embodiments or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other embodiments need not include the device itself. Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular embodiments may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various embodiments in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art. The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts. FIG. 1 illustrates an exemplary embodiment of an automated audit and recommendation system 100 according to one embodiment. The system 100 includes user device(s) 110, audit module 101, datastore 102, and a network 150 over which the various systems communicate and interact. The various computing devices described herein are exemplary and for illustration purposes only. The system may be reorganized or consolidated, as understood by a person of ordinary skill in the art, to perform the same tasks on one or more other servers or computing devices without departing from the scope of the invention. Audit module 101 receives invoice data such as invoices and/or service contracts, analyzes the invoice data to determine service inefficiencies and overages, and makes recommendations aimed at reducing customer costs. Invoice data may be any source of data for use in auditing, which may also be referred to herein as audit source data, and may not necessarily be derived solely from invoices. For example, invoice data may comprise data associated with service contracts, service quotes, terms of service, and the like, which may serve as a basis for formal invoicing at a later time. Audit module 101 obtains invoice data in formats such as portable document format (PDF), joint photographic experts group (JPG or JPEG), portable network graphics (PNG), tagged image file format (TIFF), and the like, converts invoice data into a text searchable format, as necessary, and extracts relevant information from the invoice data. In one embodiment, audit module 101 uses invoice and/or service contract templates in order to improve speed and accuracy of relevant information extraction. In one embodiment, audit module 101 may directly receive relevant information without the need to perform extraction processing such as via manual data entry of necessary relevant information via a user interface which in turn provides the relevant information to the audit module 101. Relevant information for auditing processes may comprise customer name, service provider name, service address, usage data, invoice amount, billing period, due date, taxes, fees, and the like. In general, audit module 101 is configured to audit services such as utility services (e.g. electric, gas, water, sewer, other commercial fuels, etc.), financial services (e.g. merchant processing, insurance plans such as workers' compensation or liability insurance, retirement plans, virtual card platforms), contracted services (e.g. waste removal, telecom/wireless, information technology, shipping/freight), and commercial real estate services (e.g. location/relocation assessment, property tax assessment, cost segregation, financing), however any service involving an invoice and/or service contract may be audited using the approaches disclosed herein without departing from the scope of the invention. Audit module 101 compares extracted information with a service optimization datastore to determine inefficiencies in current service, compute potential cost savings, and make recommendations to achieve the optimal service for the customer to recognize the identified cost savings. Datastore 102 stores data associated with automated audit and recommendation system 100 including but not limited to customer and user information, received invoice data, invoice and service contract templates, and recommendation reports generated by audit module 101. While depicted as a separate component, datastore 102 may be incorporated into audit module 101 without departing from the scope of the invention. User device(s) 110, also referred to herein as client device(s) 110, generally comprise a computing device which a customer or end user uses to provide invoice data and receive recommendations regarding their service. User device(s) may be used to provide invoice data in a variety of file formats including but not limited to those listed above or may be used to manually enter the relevant information such as entering the relevant information in appropriate fields via a user interface. Client device(s) 110 include, generally, a computer or computing device including functionality for communicating (e.g., remotely) over a network 150. Data may be collected from client devices 110, and data requests may be initiated from each client device 110. Client device(s) 110 may be a server, a desktop computer, a laptop computer, personal digital assistant (PDA), an in- or out-of-car navigation system, a smart phone or other cellular or mobile phone, or mobile gaming device, among other suitable computing devices. Client devices 110 may execute one or more client applications, such as a web browser (e.g., Microsoft Windows Internet Explorer, Mozilla Firefox, Apple Safari, Google Chrome, and Opera, etc.), or a dedicated application to submit user data, or to make prediction queries over a network 150. In particular embodiments, each user device 110 may be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functions implemented or supported by the user device 110. For example and without limitation, a user device 110 may be a desktop computer system, a notebook computer system, a netbook computer system, a handheld electronic device, or a mobile telephone. The present disclosure contemplates any user device 110. A user device 110 may enable a network user at the user device 110 to access network 150. A user device 110 may enable its user to communicate with other users at other user devices 110. A user device 110 may have a web browser, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLA FIREFOX, and may have one or more add-ons, plug-ins, or other extensions, such as TOOLBAR or YAHOO TOOLBAR. A user device 110 may enable a user to enter a Uniform Resource Locator (URL) or other address directing the web browser to a server, and the web browser may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to server. The server may accept the HTTP request and communicate to the user device 110 one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. The user device 110 may render a web page based on the HTML files from server for presentation to the user. The present disclosure contemplates any suitable web page files. As an example and not by way of limitation, web pages may render from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a web page encompasses one or more corresponding web page files (which a browser may use to render the web page) and vice versa, where appropriate. The user device 110 may also include an application that is loaded onto the user device 110. The application obtains data from the network 150 and displays it to the user within the application 533 interface. Exemplary user devices are illustrated in some of the subsequent figures provided herein. This disclosure contemplates any suitable number of user devices, including computing systems taking any suitable physical form. As example and not by way of limitation, computing systems may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computing system may include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computing systems may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computing systems may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computing system may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate. Network cloud 150 generally represents a network or collection of networks (such as the Internet or a corporate intranet, or a combination of both) over which the various components illustrated in FIG. 1 (including other components that may be necessary to execute the system described herein, as would be readily understood to a person of ordinary skill in the art). In particular embodiments, network 150 is an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a metropolitan area network (MAN), a portion of the Internet, or another network 150 or a combination of two or more such networks 150. One or more links connect the systems and databases described herein to the network 150. In particular embodiments, one or more links each includes one or more wired, wireless, or optical links. In particular embodiments, one or more links each includes an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a MAN, a portion of the Internet, or another link or a combination of two or more such links. The present disclosure contemplates any suitable network 150, and any suitable link for connecting the various systems and databases described herein. The network 150 connects the various systems and computing devices described or referenced herein. In particular embodiments, network 150 is an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a metropolitan area network (MAN), a portion of the Internet, or another network 421 or a combination of two or more such networks 150. The present disclosure contemplates any suitable network 150. One or more links couple one or more systems, engines or devices to the network 150. In particular embodiments, one or more links each includes one or more wired, wireless, or optical links. In particular embodiments, one or more links each includes an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a MAN, a portion of the Internet, or another link or a combination of two or more such links. The present disclosure contemplates any suitable links coupling one or more systems, engines or devices to the network 150. In particular embodiments, each system or engine may be a unitary server or may be a distributed server spanning multiple computers or multiple datacenters. Systems, engines, or modules may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, or proxy server. In particular embodiments, each system, engine or module may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by their respective servers. For example, a web server is generally capable of hosting websites containing web pages or particular elements of web pages. More specifically, a web server may host HTML files or other file types, or may dynamically create or constitute files upon a request, and communicate them to clients devices or other devices in response to HTTP or other requests from clients devices or other devices. A mail server is generally capable of providing electronic mail services to various clients devices or other devices. A database server is generally capable of providing an interface for managing data stored in one or more data stores. In particular embodiments, one or more data storages may be communicatively linked to one or more servers via one or more links. In particular embodiments, data storages may be used to store various types of information. In particular embodiments, the information stored in data storages may be organized according to specific data structures. In particular embodiment, each data storage may be a relational database. Particular embodiments may provide interfaces that enable servers or clients to manage, e.g., retrieve, modify, add, or delete, the information stored in data storage. The system may also contain other subsystems and databases, which are not illustrated in FIG. 1, but would be readily apparent to a person of ordinary skill in the art. For example, the system may include databases for storing data, storing features, storing outcomes (training sets), and storing models. Other databases and systems may be added or subtracted, as would be readily understood by a person of ordinary skill in the art, without departing from the scope of the invention. FIG. 2 illustrates an exemplary embodiment of an audit module 101 in accordance with the invention. Audit module 101 comprises invoice data interface 201, invoice data preparation unit 202, template unit 203, extraction unit 204, evaluation and recommendation unit 205, extracted information datastore 206, and service optimization datastore 207. Other systems, units, interfaces, engines and datastores may be used, as would be readily understood by a person of ordinary skill in the art, without departing from the scope of the invention. Invoice data interface 201 obtains invoice data for audit analysis. Invoice data generally refers to data associated with services received by, used by or provided to an entity (such as an individual or business). Invoice data, which may also be referred to herein as audit source data, may be any source of data for use in auditing, and may not necessarily be derived solely from invoices. For example, invoice data may comprise data associated with service contracts, service quotes, terms of service, service statements, and other service related documentation which may serve as a basis for formal invoicing at a later time. Invoice data interface 201 may obtain invoice data via various approaches comprising, but not limited to, email, web portal upload/download, and directly from a service provider (with customer authorization if required). Invoice data interface 201 may obtain invoice data in various formats suitable for further processing comprising, but not limited to, portable document format (PDF), joint photographic experts group (JPG or JPEG), portable network graphics (PNG), tagged image file format (TIFF), and the like. In this way, customers are afforded various options for providing invoice data such as downloading from a service provider, scanning hard copies or capturing images of hard copies with a smartphone or tablet, then emailing or uploading the documents for audit processing. In one aspect, invoice data interface 201 may obtain invoice data in the form of at least one of a database entry and an XML file. Invoice data preparation unit 202 prepares obtained invoice data for information extraction, as necessary. Invoice data preparation unit 202 performs analysis of the obtained invoice data and performs appropriate modifications to prepare invoice data for data extraction. In one aspect of the invention, invoice data preparation unit 202 analyzes invoice data to determine that invoice data is in a text searchable format and/or that text is present. If invoice data is not in text searchable format, invoice preparation unit 202 may perform at least one of optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, text based character recognition, and text based word recognition in order to convert audit data source data into searchable text format for additional processing, such as information extraction and/or identification. Invoice data preparation unit 202 may determine if any perspective distortion correction is required and may performs geometric rectification as necessary. Invoice data preparation unit 202 may determine at least one of locations and sizes of text and/or margins. Invoice data preparation unit 202 may compare at least one of size and location of text and/or margins with a template to determine that invoice data is in a suitable format for additional processing. In one aspect, invoice data preparation unit 202 may perform necessary adjustments (e.g. move, stretch, and/or shrink content, etc.) so at least one of margin and/or text sizes and/or locations fall within the expected bounds of a given template. Different service providers are likely to have different invoice or service document formats. Therefore, in one aspect, invoice data preparation unit 202 analyzes the text format of invoice data to determine a service provider, selects a corresponding template based on that service provider, and performs comparison and preparation functions according to that template to adjust the invoice data as necessary to fall within the expected criteria of the template for that service provider. Any of these functions listed above may be optional depending on the particular application, may be done in any order, and invoice preparation unit 202 need not perform all of these preparation functions in any one embodiment. Extraction unit 204 identifies and/or extracts relevant information from invoice data and may optionally store the relevant information in a datastore for later reference and/or processing. Relevant information may comprise at least one of at least one entity characteristic and at least one payment obligation characteristic. Exemplary entity characteristics which may be identified and/or extracted comprise at least one of entity name, entity address(es), among other characteristics as would be apparent to one of ordinary skill in the art. Exemplary payment obligation characteristics which may be identified and/or extracted comprise at least one of service type, service provider, payment amount (e.g. invoiced amount, total amount due, minimum payment amount, etc.), service period (e.g. billing cycle/period, dates service was provided/received), service usage amount (e.g. for utility services, usage could be a quantity of energy used, quantity of water used, etc.), taxes, credits, and fees, among other characteristics as would be apparent to one of ordinary skill in the art. Extraction unit 204 may perform at least one of document understanding, intelligent document processing, keyword searching, and text parsing to identify and/or locate relevant information. Keyword searching may include terms, symbols and formats indicative of relevant information, such as terms like “amount due”, “total due”, “due date”, “$” symbol, “account number”, “tax”, “fee”, “service address”, areas with formatting indicative of an address (e.g. city, state abbreviation, zip code sequence), phone number formats, usage indicators such as “kWh”, “CCF”, “Btu”, “Gals” etc. In one aspect, extraction unit 204 may identify category titles and optionally a corresponding location of associated category fees and/or charges in the invoice data. For example, in merchant processing applications, a merchant processing statement or invoice may comprise categories such as “Pending Interchange Charges”or “Pending Financial Charges and Fees”. Extraction unit 204 may identify these category terms/titles and subsequently identify corresponding costs associated with each category. In one aspect, extraction unit 204 determines which keyword search terms to look for based on the category(ies) identified. These search terms or exemplary applications are not intended to be limiting, but merely exemplary, and other search terms, symbols and formats are included within the scope of the invention as would be apparent to one of ordinary skill in the art depending on the particular service type and associated invoices, contracts, quotes, statements, etc. In one aspect, as each piece of relevant information is extracted and/or identified, extraction unit 204 assigns the information to a corresponding field within an extracted information datastore 206 (e.g. spreadsheets, databases) for later processing and analysis. In another aspect, each piece of extracted relevant information may be tagged with a corresponding label so that the relevant information is categorized for later mapping to appropriate fields within the extracted information datastore 206 (e.g. spreadsheets, databases). In one aspect, extraction unit 204 may identify additional information associated with an entity. Additional information may comprise at least one of entity industry, entity geographic location(s) of operation, entity revenue, and a number of employees employed by the entity, among other characteristics as would be apparent to one of ordinary skill in the art. Extraction unit 204 may identify additional entity characteristics via use of at least one of artificial intelligence, robotic processing automation, and searching digital records (such as databases and/or websites). In one aspect, extraction unit 204 may use a web crawler to identify the industry of a customer and add this info to a corresponding field in the extracted information database. This may comprise determining a company name and using a web crawler to identify what the company does (e.g. identify business functions). Then the extraction unit 204 may classify the company as belonging to a particular industry and store this industry information in the extracted information datastore 206 in association with the customer. In one aspect, extraction unit 204 may first determine a service provider and/or service type via at least one of the methods above, then use a corresponding template from template unit 203 (if one exists for the service provider) in order to perform targeted searching and/or identification of certain keywords in designated locations as opposed to searching the entirety of the invoice data document. This can improve speed and accuracy of locating relevant information. If no template exists, extraction unit 204 may identify locations where relevant information is found during the relevant information extraction and provide such information to template unit 203 for use in building a template for the associated service provider and/or type for later use in extraction processes. As an alternative to, or in addition to, obtaining invoices and performing information extraction, audit module 101 may obtain relevant information in a more direct form such as via manual data entry. For example, customers may enter the necessary information into fields through a network portal using their user device such as through an application interface or through a web browser interface. The relevant information is received by the audit module 101 and may be used to populate the extracted information datastore 206. In this scenario, audit module 101 may invoke the functions of evaluation and recommendation unit 205 without requiring action from invoice data interface 201, invoice data preparation unit 202, template unit 203, and extraction unit 204. Template unit 203 stores and creates templates associated with invoice data such as invoice(s) and service related documents such as contract(s) and quotes. Template unit 203 may be used to manually create a template for a given service provider or service industry by establishing specific coordinates and regions within which each of the relevant pieces of information that should be extracted are found. In one embodiment, template creation is automated wherein template unit 203 uses data received from the extraction unit 204 as a result of an extraction process to build a template for a given service provider. The template unit 203 receives location information from the extraction unit 204 such as image/pixel coordinates associated with each relevant piece of information and employs artificial intelligence or machine learning using one or more datasets associated with a given service provider to create a template. Artificial intelligence or machine learning is performed in a processor of a computing system such as those disclosed herein and may employ a neural network as part of the learning and template creation process. The template unit 203 may periodically verify accuracy of and update templates based on where relevant information was located on the processed invoice data as part of the extraction process. Evaluation and recommendation unit 205 analyzes the extracted information data generated by extraction unit 204 to identify and compute overages and make recommendations to optimize a customer's service plan. Analysis may comprise cross referencing the extracted information datastore 206 with an optimization datastore 207 to identify inefficiencies and billing errors and compute potential savings if modifications were to be made. For example, in one aspect, the extracted information comprises a company name and a service address. The service address is used to identify the state where the service is provided and company name is used to identify an industry in which the client operates. The identified industry and state are then cross referenced with a database of industry-specific sales tax exemptions available in the determined state such as sales tax exemptions on electricity, natural gas or water/sewer for manufacturers. This is merely an exemplary embodiment, not intended to be limiting, and any number of pieces of extracted information may be used individually or in combination in cross-referencing with the optimization datastore 207. In some embodiments a hierarchy may be applied to prioritize the different pieces of extracted information used in the audit analysis and recommendation process. For example, in one aspect, evaluation and recommendation unit 205 first identifies service type, then industry type if available/applicable, then customer location as the main initial categories for further audit analysis. Then, based on service type, evaluation and recommendation unit 205 determines available rate structures for different service providers within that service type which provide service coverage for the identified customer location. The evaluation and recommendation unit 205 also uses customer location information in combination with industry type to determine exemptions, programs, benefits, incentives, etc. which are available to the customer for the given service type. In one aspect, the evaluation and recommendation unit 205 compares the customer service level for a given service type with a database of service levels of like-type businesses (e.g. same industry, similar usage details, etc.) in order to optimize the customer's service and make appropriate recommendations. This hierarchy is merely exemplary and various hierarchies may be applied depending on the particular application and extracted information as would be apparent to one of ordinary skill in the art. In this way, evaluation and recommendation unit 205 can determine whether a customer is receiving appropriate incentives, exemptions, the best rate plan for their usage, being charged appropriate fees, etc. and make appropriate recommendations when there is potential cost savings available to the customer. Recommendations may take the form of a generated recommendation report which can be emailed to a customer, provided in a downloadable form or displayed via a web portal or application interface, or otherwise provided to a customer. Service optimization datastore 207 comprises a plurality of records associated with various service types and service providers. Service optimization datastore 207 comprises records associated with a plurality of service types, including but not limited to utility services, financial services, contracted services, and commercial services. For each service type, service optimization datastore 207 comprises a plurality of service provider entries and data associated with the associated service. For example, for utility service providers, optimization datastore 207 comprises data associated with each service provider's fees and rate structures, while for other services, optimization datastore 207 comprises data associated with available service levels and associated fees. Additionally, service optimization datastore 207 comprises records associated with state and local municipalities and their associated programs, benefits, credits, incentives, exemptions, etc., some of which may be tied to particular industries. Audit module 101 may use a web crawler to periodically update the optimization datastore. For example, in the context of utility service providers, a web crawler may periodically capture updated rate structures published on service provider websites and update the service optimization datastore 207 with the updated rate structure information. In one aspect, a web crawler is used to periodically update programs, benefits, credits, incentives, exemptions and the like associated with state and local municipalities within the optimization datastore 207. In one embodiment, audit module 101 applies the above concepts to perform a one time analysis of single invoice data or a batch of invoice data. For example, in one embodiment, audit module 101 may request six or twelve months worth of invoices in order to perform an initial audit and make appropriate recommendations. In other embodiments, audit module 101 performs regularly repeating audit analysis. For example, audit module 101 may be configured to automatically receive new invoices from a service provider (if authorized by a customer), such as on a monthly basis, to perform ongoing audit analysis and provide appropriate recommendations over time. Alternatively, audit module 101 periodically sends reminder notifications to customers indicating that it is time to submit a new invoice or new batch of invoices for updated audit analysis. FIG. 3 illustrates an exemplary process for automated auditing and providing recommendations, in accordance with one embodiment of the invention. The process comprises obtaining invoice data 301, identifying elements in the invoice data 302, obtaining service optimization data 303, filtering the service optimization data 304, comparing the identified elements with the filtered service optimization data 305, quantifying an overage amount 306, and providing at least one of the invoice data, identified elements, and quantified overage data 307. Any of the steps listed here may be optional and the steps may be completed in various orders and/or simultaneously depending on the preferred application as would be recognized by one of ordinary skill in the art without departing from the scope of the invention. The process may be a computer implemented process wherein at least one processor is configured to execute at least the exemplary steps listed. The process begins at step 301 by obtaining invoice data such as at least one of invoice(s), service agreement(s)/contract(s), terms of service, and/or service quote(s). Invoice data generally refers to data associated with services received by, used by or provided to an entity (such as an individual or business). Invoice data may be any source of data for use in auditing, which may also be referred to herein as audit source data, and may not necessarily be derived solely from invoices. For example, invoice data may comprise data associated with service contracts, service quotes, terms of service, and the like, which may serve as a basis for formal invoicing at a later time. Invoice data may be obtained in a digital format. Invoice data may be obtained in various forms as would be apparent to one of ordinary skill in the art. For example, invoice data may comprise at least one of uploaded files, imported files, emailed files, scanned documents, images, and raw invoice data. In one aspect, invoice data may be received in various forms including file formats such as portable document format (PDF), joint photographic experts group (JPG or JPEG), portable network graphics (PNG), tagged image file format (TIFF), and the like. In one aspect, invoice data is received in raw data format, including, but not limited to, at least one of in the form of a database entry, data entered via a user interface, and data in an XML file. Step 301 may be performed by, or in association with, a computing device such as invoice data interface 201 of audit module 101 as described in association with FIG. 2 above. At step 302, the process comprises identifying at least one element in the invoice data relevant for auditing purposes. In one aspect identifying comprises identifying a plurality of elements in the invoice data. In one aspect, the identifying at least one element comprises computationally recognizing the at least one element. The at least one element may comprise at least one of an entity characteristic(s) and a payment obligation characteristic(s). Entity characteristics may comprise characteristics associated with the entity receiving or using the associated service. The entity may be at least one of a business, a business facility, an individual, a group of individuals, a residence, etc. Exemplary entity characteristics may comprise, but are not limited to, at least one of entity name, entity address(es), entity industry, entity geographic location(s) of operation, entity revenue, and a number of employees employed by the entity. Payment obligation characteristics may comprise at least one of service type, service provider, payment amount (e.g. invoiced amount, total amount due, minimum payment amount, etc.), service period (e.g. billing cycle/period, dates service was provided/received), and service usage amount (e.g. for utility services, usage could be a quantity of energy used, quantity of water used, etc.). In one aspect, identifying elements in the invoice data comprises identifying at least one of entity name and entity address, then subsequently determining additional entity information using the entity name and/or address. The additional entity information may comprise at least one of entity industry, entity geographic location of operation, entity revenue, and a number of employees employed by the entity. In one aspect, additional entity information may be determined by automatically searching digital records such as databases and websites associated with the entity name and/or address, such as via use of a web crawler to search for and locate relevant information. In one aspect, additional entity information may be determined using at least one of artificial intelligence and robotic process automation. In one aspect, identifying elements in the invoice data comprises preparing the invoice data for information extraction/identification. Preparing the invoice data may comprise converting invoice data into a text searchable format, if necessary, and modifying invoice data location and size, if necessary. In one aspect, preparing the invoice data may comprise performing at least one of OCR, optical word recognition, intelligent character recognition, intelligent word recognition, text based character recognition, and text based word recognition to convert invoice data into text searchable format, if the received format was not text searchable. In one aspect, identifying elements in the invoice data comprises at least one of document understanding and intelligent document processing. In one aspect, if the location and size of content within the invoice data is not within expected thresholds, the invoice data may be modified to meet expected thresholds such as expected thresholds for use in performing computational recognition of relevant information. For example, content of invoice data as a whole can be moved so that margins fall within an expected threshold for computational analysis. As another example, content of the invoice data can be stretched or shrunk to fit within an expected footprint for computational analysis. In one aspect, identifying elements in the invoice data comprises extracting relevant information from invoice data by identifying relevant information within invoice data and associating this information with appropriate fields within an extracted information database. In one embodiment, extracting comprises at least one of document understanding, intelligent document processing, keyword searching, and text parsing in order to identify particular terms, symbols, values or other elements of interest. In one embodiment, extracting relevant information uses a template associated with the service provider associated with the invoice data. Relevant information to be extracted comprises, but is not limited to customer name, service provider name, service address, usage data, invoice amount, billing period, taxes, fees, etc. Keyword search includes terms, symbols and formats indicative of relevant information such as terms like “amount due”, “total due”, “due date”, “$” symbol, “account number”, “tax”, “fee”, “service address”, areas with formatting indicative of an address (e.g. city, state abbreviation, zip code sequence), phone number formats, usage indicators such as “kWh”, “CCF”, “Btu”, “Gals” etc. In one aspect, identifying elements may comprise identifying category titles and optionally a corresponding location of associated category fees and/or charges with respect to the category title. For example, in merchant processing applications, a merchant processing statement or invoice may comprise categories such as “Pending Interchange Charges”or “Pending Financial Charges and Fees”. These category terms/titles may be identified and subsequently used to identify corresponding costs associated with each category. In one aspect, keyword search terms to look for are determined based on the category(ies) identified in the invoice data. This list is not intended to be limiting, but merely exemplary, and other search terms, symbols and formats are included within the scope of the invention as would be apparent to one of ordinary skill in the art depending on the particular service type and associated invoice data format. In one embodiment, the identifying elements in the invoice data comprises a first step of determining at least one of a service provider and service type associated with invoice data, then determining a template based on the service provider and/or service type, then identifying additional relevant information using the template. In one aspect, the template indicates locations or regions within invoice data where different pieces of relevant information are expected to be found for that service provider. Templates may be developed over time by using artificial intelligence and machine learning approaches using at least one of invoice data received over time from customers and sample invoices or blank invoice forms obtained from various service providers. For example, the spatial distribution of the plurality of elements in the invoice data may be stored in association with the invoice information contained therein. This spatial distribution and corresponding information may be used to create templates based on at least one of service provider and service type for use in future processing of invoice data to aid in the identifying of elements in the invoice data. In one aspect, a database of templates may be created to store templates as they are generated such that they are available for later use. Step 302 may be performed by, or in association with, a computing device such as at least one of invoice data preparation unit 202, template unit 203, and extraction unit 204 of audit module 101 as described in association with FIG. 2 above. At step 303, the process may comprise obtaining service optimization data which generally comprises data associated with costs and/or usage associated with various services. Service optimization data may comprise at least one of rate schedules, rate plans, incentives, exemptions, and credits associated with various services. In one aspect, obtaining service optimization data may comprise obtaining only service
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