Patent Publication Number: US-11386435-B2

Title: System and method for global third party intermediary identification system with anti-bribery and anti-corruption risk assessment

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority to U.S. Provisional Patent Application No. 62/480,869, filed on Apr. 3, 2017, the entirety of which is incorporated by reference hereby. 
    
    
     TECHNICAL FIELD AND DESCRIPTION OF THE RELATED TECHNOLOGY 
     As businesses move into new markets, they have to rely on local third party intermediaries, or TPIs. Certain countries even require foreign businesses to retain local TPIs. However, with the benefits from TPIs, also comes the compliance risk in bribery and corruption. Ninety percent (90%) of US Foreign Corrupt Practices Act (FCPA) investigations brought by the US Department of Justice involve TPIs. Government regulations (e.g.: US FCPA, Anti-Money Laundering rules, UK Bribery Act) penalize businesses for violations by third parties, even when businesses are not aware of such activities, especially when no compliance procedures are found in place. 
     Companies first face the difficulties to identify among its suppliers, distributers, service providers and other potential TPIs that do business with local governments. TPIs are exposed more to compliance risks. What is needed is a tool to identify and provide information on general compliance risk assessment of likely TPIs. 
     Currently, companies have to use manual methods to have individual incoming/new third party companies (existing ones are too large to handle) self-report whether they are TPIs, for example with questionnaires, and manually go through each questionnaire to verify. 
     Not only is this method slow, expensive and covers only new third parties, there are critical flaws. For example, all TPIs found this way are self-reported, leaving out those unreported TPIs who are more likely to be at high compliance risk. Companies either have to randomly select small part of third parties because of budget constraints and miss most risky third parties, or though they may be able to cover their small third party base via manual methods, they have to waste their efforts on non-TPIs or on low risk third parties. 
     SUMMARY 
     A Third Party Intermediary (TPI) is a business entity (an agent, service provider, consultant, contractor, or other third party) employed or engaged or retained to assist a company in any function of the business that requires or involves interaction with any level of government in any of the countries. 
     Disclosed are embodiments of technology for an identification system that flags business entities that are current or future TPIs. In at least one embodiment, the system is configured for TPI identification to enable companies to optimize compliance efforts, for example, effective anti-bribery and anti-corruption compliance. The system is configured to identify TPI likelihood and General Compliance Risk (GCR) Rating to enable businesses to setup anti-bribery and anti-corruption (ABAC) strategies to focus on those high-risk TPIs, perform due diligence, and mitigate compliance risk. 
     In at least one embodiment, the system is configured with a dual-engine structure configured for optimal speed and data processing by employing parallel processing, to identify and provide risk ratings for likely TPIs. 
     The first engine is a TPI identification engine configured to process data, including business entity data from a business information database, and is further configured with artificial intelligence (AI) machine learning modules for adaptive learning, including supervised and unsupervised machine learning algorithms for identifying TPIs. The TPI Engine can operate employing a mainframe computer(s) that update less frequently and handle a greater variety of data formats than massive data processors. The TPI Engine can also be configured to process “Big Data,” including business entity data, for example on a HADOOP platform, including both big data processing components and multi-purpose computer servers together with mainframe computer(s) to include processing of more complex databases. The TPI engine processors can process independently of the second GCRM Engine. 
     The second engine of the system is a for General Compliance Risk Management (GCRM) Engine configured for General Compliance Risk Scoring, and is configured to generate and apply a risk rating to each business entity. In at least one embodiment, for the purpose of consistency in cross-country risk assessment so that ratings in various countries can be compared on the same footing, the system is configured to ingest and process various public/and internal global country scores, as well as business entity data and risk scores. The second engine is configured with independent processors to process “Big Data,” including proprietary business entity data, and is further configured with artificial intelligence (AI) machine learning modules for adaptive learning, including supervised and unsupervised machine learning algorithms. 
     One advantage is that the system provides enhanced performance results, e.g., faster searching, matching, etc. that shortens the standard search time from hours or days to milliseconds. 
     Another advantage is the system provides complete databases for TPI risk analytics that solve the deficient database systems that have missing data attributes or missing values for extant data attributes. Conventional systems attempt to address database incompleteness with data vendors or manual investigation. 
     Another advantage is for compliance risk identification, assessment and mitigation, the system enables compliance efforts in anti-bribery and anti-corruption that previously required human subjective input and self-reporting, thus the systems solves the problem of faulty and subjective global ABAC compliance resulting from target information scarcity and information bias. 
     Accordingly, the present disclosure describes embodiments that significantly alleviate incompleteness and possible bias in small business entities data and in compliance data. For example, the improved databases solve at least four kinds of database deficiencies from missing data attributes or types, missing data values, data scarcity for targets, and biased information about targets. Moreover, the present disclosure provides solutions based on AI and machine learning intelligence such that, in embodiments, the system is configured to identify TPI and provide GCRM risk analysis that both replaces and improves upon prior systems and manual investigative methods, including the self-reporting described above. Without the present system, users either have to randomly select a small part of third parties because of budget constraints and miss out most risky third parties, or though they may be able to cover their small third party base, will waste their efforts on non-TPIs or on low risk third parties. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Non-limiting and non-exhaustive embodiments are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified. 
       For a better understanding, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings, wherein: 
         FIG. 1  is a system diagram of an environment in which at least one of the various embodiments can be implemented; 
         FIG. 2  shows an embodiment of a network computer that can be included in a system such as that shown in  FIG. 1 ; 
         FIG. 3  shows an embodiment of a network computer for a TPI Engine that can be included in a system such as that shown in  FIG. 1 ; 
         FIG. 4  shows an embodiment of a network computer for a GCRM Engine that can be included in a system such as that shown in  FIG. 1 ; 
         FIGS. 5A-5B  illustrate a logical architecture flow of a system in accordance with at least one of the various embodiments; 
         FIG. 6  illustrates a flowchart for a process in accordance with at least one of the various embodiments. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Various embodiments now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific embodiments by which the innovations described herein can be practiced. The embodiments can, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the embodiments to those skilled in the art. Among other things, the various embodiments can be methods, systems, media, or devices. Accordingly, the various embodiments can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense. 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “herein” refers to the specification, claims, and drawings associated with the current application. The phrase “in one embodiment” or “in an embodiment” as used herein does not necessarily refer to the same embodiment, though it can. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it can. Thus, as described below, various embodiments can be readily combined, without departing from the scope or spirit of the invention. 
     In addition, as used herein, the term “or” is an inclusive “or” operator, and is equivalent to the term “and/or,” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     As used herein, the term “Host” can refer to an individual person, partnership, organization, or corporate entity that can own or operate one or more services for providing TPI identification and risk rating (e.g., web sites, mobile applications, or the like). 
     The following briefly describes embodiments in order to provide a basic understanding of some aspects of the innovations described herein. This brief description is not intended as an extensive overview. It is not intended to identify key or critical elements, or to delineate or otherwise narrow the scope. Its purpose is merely to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     Illustrative Operating Environment 
       FIG. 1  shows components a logical architecture of system  100  of one embodiment of an environment in which embodiments of the innovations described herein can be practiced. Not all of the components may be required to practice the innovations, and variations in the arrangement and type of the components can be made without departing from the spirit or scope of the innovations. As shown, system  100  of  FIG. 1  includes local area networks (LANs)/wide area networks (WANs)-(network)  110 , wireless network  108 , client computers  102 - 105 , TPI Scoring Server Computer  112 , and Compliance Risk Server Computer  114  and Hosting Server Computer  115 . 
     In at least one of the various embodiments, Hosting Server Computer  115 , can include one or more computers, such as, network computer  300 , or the like, that host one or more applications for providing TPI identification scores and risk assessment ratings to client users. For example, hosting servers  115  can include one or more web servers providing web sites, images hosting sites or the like. In at least one of the various embodiments, Hosting Server Computer  115  can be arranged to integrate with TPI Scoring Server Computer  112 , and Compliance Risk Server Computer  114 . 
     As shown in  FIG. 1 , this system takes in information from public information sources  116  such as social media information, public media servers, information and reporting services and of the like. In at least one of the various embodiments, public information services  116  can include one or more third-party and/or external content provider services. Public information services  116  can include, for example social network platforms, media distribution platforms, court reporters, regulatory reporters, government data, or the like. In at least one of the various embodiments, public information services  115  can be arranged to integrate and/or communicate with TPI Scoring Server Computer  112 , and Compliance Risk Server Computer  114  using APIs or other communication interfaces provided by the services. For example, a content provider service can offer a HTTP/REST based interface that enables the TPI Scoring Server Computer  112  to obtain data that can be associated with business entities analyzed by the TPI Scoring Server Computer  112  or Compliance Risk Server Computer  114 . 
     In at least one of the various embodiments, Business Entity Analytics Server  118  can be one or more computers arranged to provide business entity analytics, such as, network computer  FIG. 3 , or the like. As described herein, Business Entity Analytics Server  118  can include a database of robust company/business entity data and employee data databases. Examples of Business Entity Analytics Servers  118  are described in U.S. Pat. No. 7,822,757, filed on Feb. 18, 2003 entitled System and Method for Providing Enhanced Information, and U.S. Pat. No. 8,346,790, filed on Sep. 28, 2010 and entitled Data Integration Method and System, the entirety of each of which is incorporated by reference herein. In at least one of the various embodiments, Business Entity Analytics Servers  118  can be arranged to integrate and/or communicate with TPI Scoring Server Computer  112 , and Compliance Risk Server Computer  114  using APIs or other communication interfaces provided by the services. For example, Business Entity Analytics Servers  118  service can offer a HTTP/REST based interface that enables the TPI Scoring Server Computer  112  and the Compliance Risk Server Computer  114  to obtain data that can be associated with business entities analyzed by the TPI Scoring Server Computer  112  and Compliance Risk Server Computer  114 . 
     Global Partner Server  120  can include local or regional computers that include data on local companies. Global Partner Server  120  can be configured to integrate with TPI Scoring Server Computer  112 , and Compliance Risk Server Computer  114  and/or Host Server Computer  115  using API&#39;s or other communication interfaces provided by Global Partner Servers  120 , for example, via a centralized File Transfer Protocol site/server. In an embodiment, TPI Scoring Server Computer  112 , Compliance Risk Server Computer  114 , or other computer server can include Global Data Selection (e.g. formatting), Conversion (e.g. date reporting formats (age, year, month), or Derived Variable Creation Modules (e.g.: conversion rates, derived ratios) to normalize data to factors used in analyzing data. 
     At least one embodiment of client computers  102 - 105  is described in more detail below 2. In one embodiment, at least some of client computers  102 - 105  can operate over a wired and/or wireless network, such as networks  110  and/or  108 . Generally, client computers  102 - 105  can include virtually any computer capable of communicating over a network to send and receive information, perform various online activities, offline actions, or the like. In one embodiment, one or more of client computers  102 - 105  can be configured to operate within a business or other entity to perform a variety of services for the business or other entity. For example, client computers  102 - 105  can be configured to operate as a web server or the like. However, client computers  102 - 105  are not constrained to these services and can also be employed, for example, as an end-user computing node, in other embodiments. It should be recognized that more or less client computers can be included within a system such as described herein, and embodiments are therefore not constrained by the number or type of client computers employed. 
     Computers that can operate as client computer  102 - 105  can include computers that typically connect using a wired or wireless communications medium such as personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, or the like. In some embodiments, client computers  102 - 105  can include virtually any portable personal computer capable of connecting to another computing device and receiving information such as, laptop computer  103 , smart mobile telephone  104 , and tablet computers  105 , and the like. However, portable computers are not so limited and can also include other portable devices such as cellular telephones, display pagers, radio frequency (RF) devices, infrared (IR) devices, Personal Digital Assistants (PDAs), handheld computers, wearable computers, integrated devices combining one or more of the preceding devices, and the like. As such, client computers  102 - 105  typically range widely in terms of capabilities and features. Moreover, client computers  102 - 105  can access various computing applications, including a browser, or other web-based application. 
     A web-enabled client computer can include a browser application that is configured to receive and to send web pages, web-based messages, and the like. The browser application can be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web-based language, including a wireless application protocol messages (WAP), and the like. In one embodiment, the browser application is enabled to employ Handheld Device Markup Language (HDML), Wireless Markup Language (WML), WMLScript, JavaScript, Standard Generalized Markup Language (SGML), HyperText Markup Language (HTML), eXtensible Markup Language (XML), and the like, to display and send a message. In one embodiment, a user of the client computer can employ the browser application to perform various activities over a network (online). However, another application can also be used to perform various online activities. 
     Client computers  102 - 105  can also include at least one other client application that is configured to receive and/or send content between another computer. The client application can include a capability to send and/or receive content, or the like. The client application can further provide information that identifies itself, including a type, capability, name, and the like. In one embodiment, client computers  102 - 105  can uniquely identify themselves through any of a variety of mechanisms, including an Internet Protocol (IP) address, a phone number, Mobile Identification Number (MIN), an electronic serial number (ESN), or other device identifier. Such information can be provided in a network packet, or the like, sent between other client computers, TPI Scoring Server Computer  112 , Compliance Risk Server Computer  114 , Host Server Computer  115  or other computers. 
     Client computers  102 - 105  can further be configured to include a client application that enables an end-user to log into an end-user account that can be managed by another computer, such as TPI Scoring Server Computer  112 , Compliance Risk Server Computer  114 , Host Server Computer  115  or the like. Such end-user account, in one non-limiting example, can be configured to enable the end-user to manage one or more online activities, including in one non-limiting example, search activities, social networking activities, browse various websites, communicate with other users, or the like. However, participation in such online activities can also be performed without logging into the end-user account. 
     Wireless network  108  is configured to couple client computer(s)  103 - 105  and its components with network  110 . Wireless network  108  can include any of a variety of wireless sub-networks that can further overlay stand-alone ad-hoc networks, and the like, to provide an infrastructure-oriented connection for client computer(s)  103 . Such sub-networks can include mesh networks, Wireless LAN (WLAN) networks, cellular networks, and the like. In one embodiment, the system can include more than one wireless network. 
     Wireless network  108  can further include an autonomous system of terminals, gateways, routers, and the like connected by wireless radio links, and the like. These connectors can be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of wireless network  108  can change rapidly. 
     Wireless network  108  can further employ a plurality of access technologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generation radio access for cellular systems, WLAN, Wireless Router (WR) mesh, and the like. Access technologies such as 2G, 3G, 4G, 5G, and future access networks can enable wide area coverage for mobile devices, such as client computers  103 - 105  with various degrees of mobility. In one non-limiting example, wireless network  108  can enable a radio connection through a radio network access such as Global System for Mobil communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Wideband Code Division Multiple Access (WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the like. In essence, wireless network  108  can include virtually any wireless communication mechanism by which information can travel between client computers  103 - 105  and another computer, network, and the like. 
     Network  110  is configured to couple network computers with other computers and/or computing devices, including, TPI Scoring Server Computer  112 , Compliance Risk Server Computer  114 , Host Server Computer  115 , client computers  102 - 05  through wireless network  108 . Network  110  is enabled to employ any form of computer readable media for communicating information from one electronic device to another. Also, network  110  can include the Internet in addition to local area networks (LANs), wide area networks (WANs), direct connections, such as through a universal serial bus (USB) port, other forms of computer-readable media, or any combination thereof. On an interconnected set of LANs, including those based on differing architectures and protocols, a router acts as a link between LANs, enabling messages to be sent from one to another. In addition, communication links within LANs typically include twisted wire pair or coaxial cable, while communication links between networks can utilize analog telephone lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, and/or other carrier mechanisms including, for example, E-carriers, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communications links known to those skilled in the art. Moreover, communication links can further employ any of a variety of digital signaling technologies, including without limit, for example, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like. Furthermore, remote computers and other related electronic devices could be remotely connected to either LANs or WANs via a modem and temporary telephone link. In one embodiment, network  110  can be configured to transport information of an Internet Protocol (IP). In essence, network  110  includes any communication method by which information can travel between computing devices. 
     Additionally, communication media typically embodies computer readable instructions, data structures, program modules, or other transport mechanism and includes any information delivery media. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media. 
     One embodiment of TPI Scoring Server Computer  112  is described in more detail below in conjunction with  FIG. 2  and  FIG. 3 . Briefly, however, TPI Scoring Server Computer  112  includes any network computer capable of performing TPI scoring and machine learning analysis as described herein. Computers that can be arranged to operate as TPI Scoring Server Computer  112  include various network computers, including, but not limited to personal computers, desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, server computers, network appliances, and the like. 
     Although  FIG. 1  illustrates TPI Scoring Server Computer  112  as a single computer, the embodiments are not so limited. For example, one or more functions of the TPI Scoring Server Computer  112  can be distributed across one or more distinct network computers. Moreover, TPI Scoring Server Computer  112  can include a number of configurations. Thus, in one embodiment, TPI Scoring Server Computer  112  can contain a plurality of network computers. In another embodiment, TPI Scoring Server Computer  112  can contain a plurality of network computers that operate using a master/slave approach, where one of the plurality of network computers of TPI Scoring Server Computer  112  is operative to manage and/or otherwise coordinate operations of the other network computers. In other embodiments, the TPI Scoring Server Computer  112  can operate as a plurality of network computers arranged in a cluster architecture, a peer-to-peer architecture, and/or within a cloud architecture. Thus, embodiments are not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. 
     For example, in at least one of the various embodiments, TPI Server Computer  112  can operate employing mainframe computer(s). In an embodiment, the TPI Engine can be configured to include one or more mainframe computer network servers; the TPI Engine mainframes can be configured to update less frequently than the GCRM engine and/or other massive data processor. Thus, the independent processors for the engines require less overall processing power, and the dual processing structure can advantageously provide optimal system load without sacrificing overall speed or data processing on both paths. Moreover, a TPI engine mainframe can be configured to directly accept and process a greater variety of conventional formats for data without the need for additional data cleansing or other conversion, in contrast to a big data processing computer system. In at least one of the various embodiments, TPI Server Computer  112  can operate including both big data processing components and multi-purpose computer servers, such as a HADOOP system architecture together with mainframe computer(s) to include processing of more complex databases; the TPI engine processors can still process independently of the GCRM Server Computer. 
     One embodiment of Compliance Risk Server Computer  114  is described in more detail below in conjunction with  FIG. 2  and  FIG. 4 . Briefly, however, Compliance Risk Server Computer  114  includes virtually any network computer capable of Compliance Risk delivery. 
     Although  FIG. 1  illustrates Compliance Risk Server Computer  114  as a single computer, the various embodiments are not so limited. For example, one or more functions of the Compliance Risk Server Computer  114  can be distributed across one or more distinct network computers. Moreover, Compliance Risk Server Computer  114  is not limited to a particular configuration. Thus, in one embodiment, Compliance Risk Server Computer  114  can contain a plurality of network computers. In another embodiment, Compliance Risk Server Computer  114  can contain a plurality of network computers that operate using a master/slave approach, where one of the plurality of network computers of Compliance Risk Server Computer  114  operates to manage and/or otherwise coordinate operations of the other network computers. In other embodiments, the Compliance Risk Server Computer  114  can operate as a plurality of network computers within a cluster architecture, a peer-to-peer architecture, and/or within a cloud architecture. In an embodiment, Compliance Risk Server Computer  114  are configured in a HADOOP System. Thus, embodiments not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. 
     For example, in at least one of the various embodiments, Compliance Server Computer  114  has an independent processing system, for example a massive big data processing architecture such as a HADOOP system or other mass data processor, in order to process data independently from the TPI engine for, inter alia, parallel processing. The Compliance Server Computer  114  can be configured for committed big data processing such that a GCRM Engine, databases, and AI master matching table can take in the larger complex data set for optimal risk assessment. 
     One embodiment of Host Server Computer  115  is described in more detail below in conjunction with  FIG. 2 . Host Server Computer  115  includes virtually any network computer capable of integrating and further processing results from TPI Server Computer  112 , Compliance Server Computer  114  as well as interfacing with client computers. Computers that can be arranged to operate as Host Server Computer  115  include various network computers desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, server computers, network appliances, and the like. 
     Although  FIG. 1  illustrates Host Server Computer  115  as a single computer, the various embodiments are not so limited. For example, one or more functions of the Host Server Computer  115  can be distributed across one or more distinct network computers. Moreover, Host Server Computer  115  is not limited to a particular configuration. Thus, in one embodiment, Host Server Computer  115  can contain a plurality of network computers. In another embodiment, Host Server Computer  115  can contain a plurality of network computers that operate using a master/slave approach, where one of the plurality of network computers of Compliance Risk Server Computer  114  operates to manage and/or otherwise coordinate operations of the other network computers. In other embodiments, the Host Server Computer  115  can operate as a plurality of network computers within a cluster architecture, a peer-to-peer architecture, and/or within a cloud architecture. In an embodiment, Host Server Computer  115  are configured in a HADOOP System. Thus, embodiments not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. 
     For example, in at least one of the various embodiments, Host Server Computer  115  includes a massive big data processing architecture such as a HADOOP system or other mass data processor, in order to process data from both a TPI engine of a TPI Server Computer  112  and a GCRM Engine of a Compliance Risk Server Computer  114  as well as generating and serving combined scoring and batch processing of scores. 
     Illustrative Network Computer 
       FIG. 2  shows one embodiment of a network computer  300 . Network computer  300  can include many more or less components than those shown. The components shown, however, are sufficient to disclose an illustrative embodiment. Network computer  300  can be configured to operate as a server, client, peer, a host, or any other computer. Network computer  300  can represent, for example TPI Scoring Server Computer  112 , Compliance Risk Server Computer  114  and/or Host Computer  115 , of  FIG. 1 , and/or other network computers. As described herein, TPI Scoring Server Computer  112  and Compliance Risk Server Computer  114  are each configured with their own logical processing for parallel processing as described herein. 
     Network computer  300  includes processor  302 , processor readable storage media  328 , network interface unit  330 , an input/output interface  332 , hard disk drive  334 , video display adapter  336 , and memory  326 , all in communication with each other via bus  338 . In some embodiments, processor  302  can include one or more central processing units. 
     As illustrated in  FIG. 2 , network computer  300  also can communicate with the Internet, or some other communications network, via network interface unit  330 , which is constructed for use with various communication protocols including the TCP/IP protocol. Network interface unit  330  is sometimes known as a transceiver, transceiving device, or network interface card (NIC). 
     Network computer  300  also comprises input/output interface  332  for communicating with external devices, such as a keyboard, or other input or output devices not shown in  FIG. 2 . Input/output interface  332  can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like. 
     Memory  326  generally includes RAM  304 , ROM  322  and one or more permanent mass storage devices, such as hard disk drive  334 , tape drive, optical drive, and/or floppy disk drive. Memory  326  stores operating system  306  for controlling the operation of network computer  300 . Any general-purpose operating system can be employed. Basic input/output system (BIOS)  324  is also provided for controlling the low-level operation of network computer  300 . 
     Although illustrated separately, memory  326  can include processor readable storage media  328 . Processor readable storage media  328  can be referred to and/or include computer readable media, computer readable storage media, and/or processor readable storage device. Processor readable storage media  328  can include volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of processor readable storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media which can be used to store the desired information and which can be accessed by a computer. 
     Memory  326  further includes one or more data storage  308 , which can be utilized by network computer  300  to store, among other things, applications  312  and/or other data such as content  310 . For example, data storage  308  can also be employed to store information that describes various capabilities of network computer  300 . The information can then be provided to another computer based on any of a variety of events, including being sent as part of a header during a communication, sent upon request, or the like. Data storage  308  can also be employed to store messages, web page content, or the like. At least a portion of the information can also be stored on another component of network computer  300 , including, but not limited to processor readable storage media  328 , hard disk drive  334 , or other computer readable storage medias (not shown) within client computer  300 . 
     Data storage  308  can include a database, text, spreadsheet, folder, file, or the like, that can be configured to maintain and store user account identifiers, user profiles, email addresses, IM addresses, and/or other network addresses; or the like. 
     Data storage  308  can further include program code, data, algorithms, and the like, for use by a processor, such as processor  302  to execute and perform actions. In one embodiment, at least some of data store  308  might also be stored on another component of network computer  300 , including, but not limited to processor-readable storage media  328 , hard disk drive  334 , or the like. 
     Data storage  308  can also include a business entity information database  318  of robust company/business entity data to map company firmographic data from a business entity analytics server  118  as described herein. In at least one of the various embodiments, the firmographic data from the business entity information database  318  can include scores or other rankings for companies. In at least one of the various embodiments the business entity information database  318  can include one or more databases linked to business entity information, for example an employee database including employee names and title or management code, a contact database of contacts for employees of companies (e.g. email, mobile device IDs, phone), or a cookie database of data compiled from cookies a business entity uses for digital tracking and digital campaigns. Data from database  318  can be mapped to companies using a mapping module  508 . In at least one of the various embodiments, if the company name is not identified in the business entity database the system can be configured to generate an identifier and a generate one or more firmographic databases for the company. Non limiting examples of company entity data linking, generating firmographic databases and scoring for companies, and data integration by business entity information database  304  and business analytics server are described in U.S. Pat. No. 7,822,757, filed on Feb. 18, 2003 entitled System and Method for Providing Enhanced Information, and U.S. Pat. No. 8,346,790, filed on Sep. 28, 2010 and entitled Data Integration Method and System, the entirety of each of which is incorporated by reference herein. 
     Data Storage  308  can also include a classifier data store  319  comprising a set of classifiers, which can be provided to a TPI master matching table  402  and GCRM master matching table  422  for AI enabled score and rating as described herein. 
     Applications  312  can include computer executable instructions, which can be loaded into mass memory and run on operating system  306 . Examples of application programs can include transcoders, schedulers, calendars, database programs, word processing programs, Hypertext Transfer Protocol (HTTP) programs, customizable user interface programs, IPSec applications, encryption programs, security programs, SMS message servers, IM message servers, email servers, account managers, and so forth. Applications  312  can also include website server  314 , classifier builder  317 , and Applications  312  for a TPI Engine Computer Server  112  ( FIG. 3 ), and a General Compliance Risk Management (GCRM) Server Computer ( FIG. 4 ). 
     Website server  314  can represents any of a variety of information and services that are configured to provide content, including messages, over a network to another computer. Thus, website server  314  can include, for example, a web server, a File Transfer Protocol (FTP) server, a database server, a content server, or the like. Website server  314  can provide the content including messages over the network using any of a variety of formats including, but not limited to WAP, HDML, WML, SGML, HTML, XML, Compact HTML (cHTML), Extensible HTML (xHTML), or the like. 
     Applications can include a classifier model building application  317 . Artificial Intelligence (AI) machine learning and processing, including AI machine learning classification can be based on any of a number of known machine learning algorithms, including classifiers such as the classifiers described herein (e.g., cluster, K-means cluster, decision tree, random forest decision tree, gradient boosted trees machine, propositional rule learner, linear regression, neural nets, synthetic minority over-sampling technique (SMOTE), support vector machines (SVM), etc.). 
     In at least one embodiment, the system AI is configured to identify likely TPIs by correlation with known TPIs. Even where a number of known TPIs is small or scarce, the TPI engine can employ the master matching table and AI machine learning engine to identify likely TPIs. For example, where initially there are 69 known TPIs available from past compliance efforts in a large global third party portfolio, conventional risk analytics would be unable to use such a small number assess compliance risk. Accordingly, in an embodiment, the system can be configured supervised machine learning employing a AI classifier, for example a Balanced Random Forest, SMOTE, SVM, Neural Nets, etc. 
     For example, in an embodiment, the system can be configured to employ a clustering algorithm on a training database to group companies identified as TPIs and unidentified companies to generate classes and classifiers for TPI identification. The classifier results can then be compared against databases of known TPI companies to confirm accuracy and refine the classifiers. In at least one of the various embodiments Training databases can be compiled by mapping business entity data from business entity databases and other databases as described herein to pre-identified TPI companies to create weights for factors in TPI identification, for example, firmographics, business entity analytical scoring, past financial statements (e.g.: 5 year window), legal indicators and Business Linkage information. 
     In embodiments, different machine learning algorithms can be run on the same database and then the results confirmed by supervision to identify the best AI for different datasets, as described herein. 
     For example, in training a classifier for a TPI engine&#39;s master matching table, it was found that where a given region or company is correlated with the company, in one embodiment, the TPI engine can employ a Random Forest algorithm, which is used for 70% of regions identified, where the remaining 30% of regions employ a Scorecard algorithm classifier because of data quality and data coverage. 
     In another example, in training classifiers for a GCRM engine, it was found, in one embodiment, that the GCRM engine can employ a Random Forest algorithm for 70% of regions identified, where the remaining 30% of regions can be split between SVM and Gradient Boosting The system can also be configured to use AI to determine which classifiers produce the better scoring. Moreover, over time classifiers can be periodically reapplied and results reconfirmed to find the best classifiers for additional and changing data sets and windows. 
     As shown in  FIG. 3 , Applications  312  for a TPI Computer Server  112  comprises a Third Party Identification Engine  412 , TPI master matching table  402 , TPI classifier AI store  404 , and TPI Scoring Generator  406 . Applications  312  for a Third Party Identification Engine  412  can employ processes, or parts of processes, similar to those described in conjunction with  FIGS. 5A-6 , to perform at least some of its actions. In an embodiment, processes and applications shown for the TPI Computer Server  112  can configured for standard mainframe computers and supercomputers due to the standard compatibility with, inter alia, partner global server computers  120  and public computers. In an embodiment, these are run in parallel with separate processors from the General Compliance Risk Computer server  114 , as the latter is configured for massive data processing, for example in a HADOOP system, due to the massive data processing involved for GCRM Engine applications. 
     As shown in  FIG. 4 , Applications  312  for a General Compliance Risk Computer server  114  comprise a GCRM Engine  422 , GCRM master matching table  422 , CGRM classifier AI store  424 , and a GCRM Scoring Generator  426  Applications  312  for a General Compliance Risk Computer server  114  can employ processes, or parts of processes, similar to those described in conjunction with  FIGS. 5A-6 , to perform at least some of its actions. In an embodiment, processes and applications shown for General Compliance Risk Computer server  114  is configured for massive data processing, for example in a HADOOP system, due to the massive data processing involved for GCRM Engine applications. In an embodiment, these are run in parallel with separate processors from the TPI Computer Server  112  comprising a Third Party Identification Engine  412 , as the latter can be configured to run by including standard computer and supercomputer mainframes. 
       FIG. 5A  represents a logical architecture for system  500  for TPI rating and GCRM Assessment scoring in accordance with at least one of the various embodiments. In at least one of the various embodiments, data from one or more sources, such as, public information source  316 , business entity information source  318 , or global partner sources  320  can be retrieved. 
     In at least one of the various embodiments, data that can be received and/or determined from one or more data sources can be processed in a mapping component  508 . In at least one of the various embodiments, where there are disparate sources of data, data events can be defined using dissimilar criteria and/or definitions. Further, in at least one of the various embodiments, the various event sources can provide event information using a variety of data formats and/or communication protocols. Thus, mapping component  508  can be arranged to map to events into one or more matching tables of the respective engines. In at least one of the various embodiments, this mapping can normalize events that can be received from disparate event sources. 
     In at least one of the various embodiments, mapping component  508  can be arranged to use one or more filters, tests, rules, regular expressions, pattern matches, lookup tables, heuristics, or the like, for mapping the determined data to the matching table. In some embodiments, mapping component  508  can be arranged to determine the particular, filters, tests, rules, regular expressions, pattern matches, lookup tables, heuristics, or the like, for mapping data based on the data source. In at least one of the various embodiments, information for each table can be stored using well-known data structures such as objects, hashes, arrays, linked lists, or the like. In at least one of the various embodiments, the information can be stored in a database and associated with each business that corresponds to the tables and scoring that is associated with the master matching table. 
     Thousands of information items can be used by the Computer Server  112  and the machine learning AI. For example, an exemplary listing of 100 illustrative data information attributes that can be employed by the system is shown in Table 1. The data can be pulled from public information source  316 , business entity information source  318 , or global partner sources  320 . Data items can cover data systems including
         1) firmgraphics,   2) financials,   3) trade/payment,   4) social media,   5) compliance violations,   6) public information   7) business entity scores and ratings       

     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 # 
                 Information 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 1 
                 Company Name 
               
               
                 2 
                 Primary Address Street Address 
               
               
                 3 
                 Primary Town/City Name 
               
               
                 4 
                 Primary County Name 
               
               
                 5 
                 Legal Form Text 
               
               
                 6 
                 Organization Operating Status Code 
               
               
                 7 
                 Employees Total 
               
               
                 8 
                 Legal Structure 
               
               
                 9 
                 Subsidiary Indicator 
               
               
                 10 
                 Primary SIC 
               
               
                 11 
                 Facility Owned/Rented 
               
               
                 12 
                 Date Started Under Current Management 
               
               
                 13 
                 Import Indicator 
               
               
                 14 
                 Export Indicator 
               
               
                 15 
                 Family Tree Member Role 1 Text 
               
               
                 16 
                 Family Tree Member Role 2 Text 
               
               
                 17 
                 Family Tree Member Role 3 Text 
               
               
                 18 
                 Family Tree Member Role 4 Text 
               
               
                 19 
                 Financial Statement Date 
               
               
                 20 
                 Total Current Assets Amount 
               
               
                 21 
                 Working Capital Amount 
               
               
                 22 
                 Employee Deduction Amount 
               
               
                 23 
                 Interest Payable Amount 
               
               
                 24 
                 Net of Tax Amount 
               
               
                 25 
                 Sales Revenue Amount 
               
               
                 26 
                 Total Current Liabilities Amount 
               
               
                 27 
                 Gross Profit/Loss Amount 
               
               
                 28 
                 Net Income 
               
               
                 29 
                 Profit Margin 
               
               
                 30 
                 Dividends 
               
               
                 31 
                 Stock Turnover (Inventory Turnover) 
               
               
                 32 
                 Intangible amortisation Amount 
               
               
                 33 
                 Depreciation (amortisation) 
               
               
                 34 
                 Solvency Ratio (industry quartile) 
               
               
                 35 
                 Current Ratio 
               
               
                 36 
                 Quick Ratio 
               
               
                 37 
                 Cost of Goods Sold 
               
               
                 38 
                 Other Operating Expense Amount 
               
               
                 39 
                 Net Profit Before Taxes 
               
               
                 40 
                 Interest Receivable Amount 
               
               
                 41 
                 Company Employee Count 
               
               
                 42 
                 Capital Stock Amount 
               
               
                 43 
                 Reserve Fund Amount 
               
               
                 44 
                 Total Fixed Assets Amount 
               
               
                 45 
                 Total Assets Amount 
               
               
                 46 
                 Net Worth Amount 
               
               
                 47 
                 Operating Cash Flow 
               
               
                 48 
                 Total Liabilities Amount 
               
               
                 49 
                 D&amp;B Data Depth 
               
               
                 50 
                 On Government Procurement List 
               
               
                 51 
                 Country Risk Rating 
               
               
                 52 
                 Perceived Corruption Index 
               
               
                 53 
                 # of adverse media coverage in last month 
               
               
                 54 
                 # of adverse media coverage in quarter 
               
               
                 55 
                 # of adverse media coverage in last 6 months 
               
               
                 56 
                 # of adverse media coverage in last year 
               
               
                 57 
                 # of adverse media coverage in last 2 years 
               
               
                 58 
                 # of adverse media coverage in last 3 years 
               
               
                 59 
                 3-month interest rate current quarter 
               
               
                 60 
                 3-month interest rate last quarter 
               
               
                 61 
                 Unemployment Rate of Country 
               
               
                 62 
                 GDP growth rate of Country 
               
               
                 63 
                 Failure Raw Score 
               
               
                 64 
                 Delinquency Raw Score 
               
               
                 65 
                 D&amp;B Rating 
               
               
                 66 
                 Credit Recommendation 
               
               
                 67 
                 Paydex Score 
               
               
                 68 
                 D&amp;B Global Business Risk score 
               
               
                 69 
                 Number of Payment Experiences with Negative comments 
               
               
                 70 
                 Number of Payment Experiences Slow-Negative 
               
               
                 71 
                 Number of Satisfactory Experiences 
               
               
                 72 
                 Total Number of Experiences with Past Due Amount 
               
               
                 73 
                 Total Amount Past Due 
               
               
                 74 
                 Total Credit Amount 
               
               
                 75 
                 Total Amount Owing 
               
               
                 76 
                 Total amount with negative comment 
               
               
                 77 
                 Total Amount with satisfactory comment 
               
               
                 78 
                 Total Prompt or Better Amount 
               
               
                 79 
                 Total Amount Slow 1-30 Days 
               
               
                 80 
                 Total Amount Slow 31-60 Days 
               
               
                 81 
                 Total Amount Slow 61-90 Days 
               
               
                 82 
                 Total Amount Slow 91-120 Days 
               
               
                 83 
                 Total Amount Slow 121-150 Days 
               
               
                 84 
                 Total Amount Slow 151-180 Days 
               
               
                 85 
                 Total Amount Slow 181+ Days 
               
               
                 86 
                 Bankruptcy Filing Type Text 
               
               
                 87 
                 Bankruptcy Filing Date 
               
               
                 88 
                 Total Open Suits Count 
               
               
                 89 
                 Total Open Suits Amount 
               
               
                 90 
                 Latest Suit Date 
               
               
                 91 
                 Latest Suit Amount 
               
               
                 92 
                 Total Open Judgments Count 
               
               
                 93 
                 Total Judgments Amount 
               
               
                 94 
                 Latest Judgment Date 
               
               
                 95 
                 Latest Judgment Amount 
               
               
                 96 
                 Total Open Claims Count 
               
               
                 97 
                 Total Open Claims Amount 
               
               
                 98 
                 Last Compliance Investigation Category 
               
               
                 99 
                 Last Compliance Investigation Start Date 
               
               
                 100 
                 Last Compliance Investigation Stage 
               
               
                 . . . 
                 . . . 
               
               
                   
               
            
           
         
       
     
     In at least one of the various embodiments, a TPI Engine  412  analysis can include determining business entity information from a data store, such as business entity information source  318 . In at least one of the various embodiments, business entity database  318  can include firmographic information (incorporation year, years current management in control, SIC/Industries, number of employees, legal structure (proprietorship), business scoring and rating information (e.g.: marketability indicator, Supplier Risk Indicator (SSI), Tier-N Supplier Chains, Importer/Exporter indicator), historical financial information (e.g. past 5 years), and Business Linkage information (e.g.: headquarters, branch, subsidiary), or the like, associated with a business entity. 
     In at least one of the various embodiments, TPI Engine  412  analysis can include determining global business entity information from a data store, such as, global partner/third party database  320 . In at least one of the various embodiments global partner/third party database  320  includes local data from global businesses, for example business names in local language, and business address including country. In an embodiment, text mining based on names in local language and in English can be employed to create Keyword Indicators that associate a TPI likelihood with different probabilities). 
     In at least one of the various embodiments, TPI Engine  412  analysis can include determining business entity information from a data store or source, such as, public information databases  316  and sources  116 . In at least one of the various embodiments public information databases and sources  320  includes legal status information (e.g.: suits, liens, judgments), social and mass media information (e.g. positive, negative, and neutral scoring of company on social networks and web, trending data, etc.). 
     In at least one of the various embodiments, GCRM Engine  412  analysis can include determining global business entity risk information from a data store, such as, global partner/third party database  320 . In at least one of the various embodiments global partner/third party database  320  risk data includes local data from global businesses, for example individuals and employees associated with companies (e.g.: company directors), compliance warnings/sanctions and stages/seriousness of compliance violations for companies. 
     In at least one of the various embodiments, GCRM Engine  412  analysis can include determining business entity information from a data store, such as, business entity database  318  from a business entity data source  118 . In at least one of the various embodiments, business entity database  318  can include firmographic information (incorporation year, years current management in control, SIC/Industries, number of employees, legal structure (proprietorship), business scoring and rating information (e.g.: marketability indicator, Supplier Risk Indicator (SSI), Tier-N Supplier Chains, Paydex, Out of Business Indicator, Secured Filling Indicator, and Country Risk Indicator), historical financial information (e.g. past 5 years), and Business Linkage information (e.g.: headquarters, branch, subsidiary), or the like, associated with a business entity. 
     In at least one of the various embodiments, GCRM Engine  412  analysis can include determining business entity information from a data store or source, such as, public information databases  316  and sources  116 . In at least one of the various embodiments public information databases and sources  320  includes legal status information (e.g.: suits, liens, judgments), social media information (e.g. positive, negative, and neutral scoring of company on social networks and web, trending data, etc.), Public information (Perceived Corruption Index, Basel Anti-Money Laundering (AML) Index, Office of Foreign Assets Control (OFAC) Sanctioned Countries, Past financial statements (Balance Sheet, Income Statement and Cash Flow Statement) with financial ratios and trends, Trade information (payments, delinquency amounts and depth (30 days past due, 90 days past due, etc.), together with past Max/Min/average credit amounts), Legal indicators (Suit, liens, judgment), Derogatory information (director previous association with bankruptcy, etc.), social and mass media information (e.g. positive, negative, and neutral scoring of company on social networks and web, trending data, etc.), and Business Linkage Info (headquarters, branch, subsidiary). 
     In at least one of the various embodiments, scores and other data from the TPI Engine  412  and the GCRM Engine  422  can flow to scoring generator  516 . In at least one of the various embodiments, scoring generator  516  can be arranged to generate one or more scores based on the TPI likelihood rating score from the TPI Engine  412  and the general compliance risk rating score from the GCRM Engine  422 . 
     In at least one of the various embodiments, the scoring information can be sent to a display interface  520  that can render a display of the information produced by the other components of the systems. In at least one of the various embodiments, a dashboard display  520  can be presented on a client computer accessed over network, such as client computers  102 - 105  or the like. 
       FIG. 5B  shows a logical architecture and flow for artificial Intelligence (AI) for machine learning and processing and classifier building. AI machine learning classification can be based on any of a number of known machine learning algorithms, including classifiers such as the classifiers described herein (e.g., cluster, K-means cluster, decision tree, random forest decision tree, gradient boosted trees machine, propositional rule learner, linear regression, neural nets, synthetic minority over-sampling technique (SMOTE), support vector machines (SVM), etc.). 
     For example, in an embodiment, the system can be configured to employ a clustering algorithm on a training database to group companies identified as TPIs and unidentified companies to generate classes and classifiers for TPI identification. The classifier results can then be compared against databases of known TPI companies to confirm accuracy and refine the classifiers. In at least one of the various embodiments training databases can be compiled by mapping business entity data from a business entity databases to pre-identified TPI companies to create weights for factors in TPI identification, for example, firmographics, business entity analytical scoring, past financial statements (e.g. 5 year window), legal indicators and Business Linkage information. 
     In embodiments, different machine learning algorithms can be run on the same database and then the results confirmed by supervision to identify the best AI for different datasets, as described herein. For example, in training a classifier for a TPI engine&#39;s master matching table, it was found that where a given region or company is correlated with the company, in one embodiment, the TPI engine can employ a Random Forest algorithm, which is used for 70% of regions identified, where the remaining 30% of regions employ a Scorecard algorithm classifier because of data quality and data coverage. 
     In at least one embodiment, at block  501 , the system AI is configured to identify likely TPI&#39;s by correlation with known TPIs. Even where a number of known TPIs is small or scarce, the TPI engine can employ a master matching table and AI machine learning engine to identify likely TPIs. 
     For example, where initially there are 69 known TPIs available from past compliance efforts in a large global third party portfolio, conventional risk analytics would be unable to use such a small number assess compliance risk. Accordingly, in an embodiment, the system can be configured with supervised machine learning employing a AI classifier, for example, a Balanced Random Forest, SMOTE, SVM, Neural Nets, etc. 
     For the illustrative example in Table 2, a Random Forest is employed to classify and capture the known TPIs. Table 2 illustrates an example where 9 groups were created and ranked by the predicted probability of TPIs from supervised machine learning; most of the known TPIs (71%) were found in Group 1. Accordingly, where known and unknown TPIs are classified by one or more TPI profiles matching Group 1, the system can classify previously unknown TPIs as predicted TPIs or inferred TPIs. Thus the system AI is configured to classify and identify known and unknown TPIs, which not only identifies likely TPIs, but also increases the pool of TPI&#39;s for further AI analysis. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Groups 
                 Records 
                 TPIs 
                 % of Records 
                 TPI Capture 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 231 
                 49 
                 1.0% 
                 71.0%  
               
               
                 2 
                 229 
                 10 
                 1.0% 
                 14.5%  
               
               
                 3 
                 237 
                 3 
                 1.0% 
                 4.3% 
               
               
                 4 
                 251 
                 4 
                 1.1% 
                 5.8% 
               
               
                 5 
                 238 
                 0 
                 1.0% 
                 0.0% 
               
               
                 6 
                 206 
                 1 
                 0.9% 
                 1.4% 
               
               
                 7 
                 261 
                 1 
                 1.1% 
                 1.4% 
               
               
                 8 
                 234 
                 1 
                 1.0% 
                 1.4% 
               
               
                 9 
                 21,836 
                 0 
                 92.0%  
                 0.0% 
               
               
                 Total 
                 23,723 
                 69 
                 100%  
                 100%  
               
               
                   
               
            
           
         
       
     
     For example, as shown in Table 3 with the addition of inferred TPIs, the system AI can employ supervised learning models, for example a Balanced Random Forest, SMOTE, SVM, Neural Nets, etc., to assign TPI Weight of Evidence (WOE) propensity scores to all third-party records. For the simplified illustrative example in Table 3, a scorecard model illustrates the assign TPI Weight of Evidence (WOE) propensity scores. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                 Standard 
                 Wald 
                 Pr &gt; 
                 Standardized 
               
               
                 Parameter 
                 Estimate 
                 Error 
                 ChiSq 
                 ChiSq 
                 Estimate 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Intercept 
                 −5.0354 
                 0.1366 
                 1358.1630 
                 &lt;.0001 
                   
               
               
                 WOE_V 
                 0.5894 
                 0.1176 
                 25.0991 
                 &lt;.0001 
                 0.6423 
               
               
                 WOE_S 
                 0.5518 
                 0.0841 
                 43.0966 
                 &lt;.0001 
                 0.4319 
               
               
                 WOE_L 
                 0.5898 
                 0.0509 
                 134.2597 
                 &lt;.0001 
                 0.3716 
               
               
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
               
               
                 WOE_M 
                 0.5942 
                 0.1235 
                 23.1616 
                 &lt;.0001 
                 0.2510 
               
               
                 WOE_P 
                 0.2959 
                 0.0707 
                 17.5331 
                 &lt;.0001 
                 0.1491 
               
               
                 WOE_K 
                 0.2930 
                 0.1114 
                 6.9097 
                 0.0086 
                 0.0348 
               
               
                   
               
            
           
         
       
     
     In an embodiment, for initial construction, to identify both reported and non-reported TPIs, based on domain knowledge, attributes can be removed and new attributes added for data values to populate and be weighted. A list of attributes from thousands of attributes (as described above with respect to illustrative attributes of Table 1) can be employed for unsupervised machine learning to segment a third-party portfolio of TPIs into homogenous clusters. For a simplified illustrative example, as shown in Table 4, an unsupervised machine learning clustering algorithm produces 9 clusters. Cluster 4 had the highest “Reported TPI” rate (33%), and captured most of Reported TPIs (77%). Because the new list of attributes for unsupervised learning was built for both reported and non-reported TPIs, all third parties in Cluster 4 are either Reported or Business Inferred (non-reported) TPIs, (after removing records where TPI exclusion rules apply). 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                   
                 Cluster ID 
                 % of Records 
                 TPI Rate 
                 % of TPIs 
               
               
                   
               
             
            
               
                   
                 1 
                 8% 
                 1% 
                 2% 
               
               
                   
                 2 
                 8% 
                 0% 
                 0% 
               
               
                   
                 3 
                 17% 
                 0% 
                 0% 
               
               
                   
                 4 
                 12% 
                 33% 
                 77% 
               
               
                   
                 5 
                 22% 
                 0% 
                 1% 
               
               
                   
                 6 
                 6% 
                 7% 
                 8% 
               
               
                   
                 7 
                 9% 
                 1% 
                 1% 
               
               
                   
                 8 
                 13% 
                 2% 
                 5% 
               
               
                   
                 9 
                 4% 
                 8% 
                 6% 
               
               
                   
               
            
           
         
       
     
     In an embodiment, the AI can be configured to include supervised learning models, using both reported and non-reported TPIs as a target, to generate unbiased TPI identification with WOE parameters that are not reliant on TPI self-identification or manual investigation. By way of simplified example, AI employing supervised learning, for example a Random Forest, generates classifications which inform grouping and WOE parameters for reported and implied TPI, as illustrated in the scorecard model shown in Table 5. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                   
                   
                 Standard 
                 Wald Chi- 
                 Pr &gt; 
                 Standardized 
               
               
                 Parameter 
                 Estimate 
                 Error 
                 Square 
                 ChiSq 
                 Estimate 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Intercept 
                 −2.0754 
                 0.0627 
                 1096.9037 
                 &lt;.0001 
                   
               
               
                 WOE_V 
                 1.1936 
                 0.0549 
                 472.2963 
                 &lt;.0001 
                 1.2880 
               
               
                 WOE_S 
                 1.3424 
                 0.0529 
                 643.5562 
                 &lt;.0001 
                 1.0040 
               
               
                 WOE_L 
                 0.9475 
                 0.0468 
                 410.0729 
                 &lt;.0001 
                 0.5789 
               
               
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
               
               
                 WOE_A 
                 0.7291 
                 0.0688 
                 112.4597 
                 &lt;.0001 
                 0.3350 
               
               
                 WOE_P 
                 0.5630 
                 0.0455 
                 152.8571 
                 &lt;.0001 
                 0.3350 
               
               
                 WOE_M 
                 0.4229 
                 0.0712 
                 35.2955 
                 &lt;.0001 
                 0.2720 
               
               
                 WOE_K 
                 0.4682 
                 0.0873 
                 28.7429 
                 &lt;.0001 
                 0.0844 
               
               
                 WOE_I 
                 0.4047 
                 0.1971 
                 4.2152 
                 0.0401 
                 0.0709 
               
               
                 WOE_R 
                 0.3393 
                 0.1382 
                 6.0279 
                 0.0141 
                 0.0518 
               
               
                   
               
            
           
         
       
     
     The system can be configured to use AI to determine which classifiers produce the better scoring. For example, as explained herein, in training classifiers for a GCRM engine, it was found, in one embodiment, that the GCRM engine can employ a Random Forest algorithm for 70% of regions identified, where the remaining 30% of regions can be split between SVM and Gradient Boosting classifier algorithms. 
     The system can also be configured for recursive iteration of the algorithm upon due diligence or other post facto validation of an inferred or likely TPI as a known TPI. Moreover, over time classifiers can be periodically reapplied and results reconfirmed to find the best classifiers for additional and changing data sets and windows. For example, as shown at block  503 , subsequent compliance due diligence efforts, combined with the AI analytics results can generate additional confirmed TPIs or first identified as predicted or inferred TPIs. For example, at block  501  the system ingests a database including entities identified as true Non-TPIs, known TPIs, and unknown entities. At block  502 , the system then identifies predicted TPIs as described herein. At block  503 , entities can be verified or validated as TPIs or not. For example, there can be new, known TPIs provided from other data sources or from other due diligence, including via conventional approaches. The new TPIs enhance the recency and purity of the existing TPI database, for example, where inferred TPIs include impurities. The new confirmation of validated true non-TPIs, known TPIs and confirmed TPIs can then be used to refine and optimize the AI for more accurate predictions. Adaptive learning in next iterations make the analytics results more reliable and accurate, as well as able to reflect real-world changes and windows for TPI identification and weighting of parameters. 
     Generalized Operation 
     The operation of certain embodiments will now be described with respect to  FIG. 6 . In at least one of various embodiments, processes  600  described in conjunction with  FIG. 6 , respectively, can be implemented by and/or executed with dual processing, for example using a network computer  300  of  FIGS. 3-5 , configured to operate as a mainframe or in a big data processing platform for respective TPI and GCRM engines as described herein. However, embodiments are not so limited, and various combinations of network computers, client computers, virtual machines, or the like can be utilized. Further, in at least one of the various embodiments, the processes described in conjunction with  FIG. 6  can be operative in system with logical architectures such as those described in conjunction with  FIGS. 1-5B . 
       FIG. 6  illustrates an overview flowchart for process  600  for generating a TPI likelihood and risk rating in accordance with at least one of the various embodiments. At block  602 , in at least one of the various embodiments, the system receives a request for a TPI likelihood rating and compliance risk for at least one company of interest. For example, a multinational supplier portfolio includes a supplier from Mexico, the ABC Company. For purposes of explanation, an example using a single company is given, however the system can be configured to perform batch processing of large portfolios to identify a large number of companies that are TPIs, which as described herein, thereby provides a system that objectively and independently identifies TPI likelihood and compliance risk. As noted herein, this previously could only be done manually and with voluntary self-reporting, thus constraining compliance efforts in both number of companies identified and validation. 
     At block  604 , in at least one of the various embodiments, a TPI Engine receives at least the company name for the request. In at least one of the various embodiments, the system receives a company name and address, which goes to the TPI Engine. In at least one of the embodiments, the system may receive the company name and/or address system host&#39;s native language (e.g. English for a U.S. host) or in local language from another region or country (e.g., non-English). For example, the TPI Engine receives the request for analysis of the ABC company to generate a TPI score and compliance risk assessment rating for ABC Company. As described herein, the TPI Engine has own processing system in order to process data independently from the GCRM engine for, inter alia, parallel processing. In an embodiment, the TPI Engine can be configured to include one or more mainframe computer network servers; the TPI Engine mainframes can be configured to update less frequently than the GCRM engine and/or other massive data processor. Thus, the independent processors for the engines require less overall processing power, thus the dual processing structure can advantageously provide optimal system load without sacrificing overall speed or data processing on both paths. Moreover, a TPI engine mainframe can be configured to directly accept and process a greater variety of conventional formats for data without the need for additional data cleansing or other conversion, in contrast to big data processing computers (e.g. a HADOOP system for a GCRM engine or other TPI data processing). 
     A block  606  the TPI engine receives business entity data from a global business partner/3d party database(s)  320 , a business entity information database(s)  318 , and/or public data database(s)  316 . For example, in an embodiment the TPI engine accesses or receives data from at least business entity database  318  from business entity analytics servers  118 . 
     At block  608 , in at least one of the various embodiments, the information for the identified business entity is correlated with the business information from at least one of the databases. For example, the ABC company can be identified in the business entity database, from which the TPI engine identifies that the ABC company is a new business partnership legal consulting firm with 6 employees; the ABC company is not involved in global business and all business is local to Mexico. The business name, street address and telephone number are all found in the business entity database(s)  318 . The TPI engine also determines one partner of ABC company is associated with a business bankruptcy in last 7 years; this data can come from the business entity database  318  or from public information databases. The TPI engine can also receive business scoring and rating data from the business entity database  318 . The business entity analytics server  112  analyzes business data to provide scores and ratings. For example, the business entity analytics server  112  may provide country risk ratings (e.g.: Mexico Country Risk Rating by the business entity analytics server is 3.75, which is relatively high). 
     At block  610 , in at least one of the various embodiments, the TPI engine identifies factors for the company to determine the business entity is a TPI. For example, as noted above, the TPI engine also determined one partner of ABC company is associated with a business bankruptcy in last 7 years and that Mexico has a Country Risk Rating from the business entity analytics server of 3.75. These factors, together with information such as ABC is a new company with 6 employees, are examples of factors that can indicate a company is a TPI. As explained herein, such factors can number in the thousands. 
     At block  612 , in at least one of the various embodiments, the TPI engine processing the business entity identification data, correlated business information, and the identification data using the TPI master matching table and processing module. In an embodiment, the master matching table includes one or more machine learning AI engine classifiers. In at least one embodiment, the system AI is configured to identify likely TPI&#39;s by correlation with known TPIs. As described herein, even where a number of known TPIs is small or scarce, the TPI engine can employ the master matching table and AI machine learning engine to identify likely TPIs. The classifier can be selected from a supervised machine learning engine, a non-supervised machine learning engine, or both. For example, the TPI engine&#39;s master matching table can include a Random Forest and SVM (Support Vector Machine), for example for a binary file table (for machine read only). For another example, the TPI engine&#39;s master matching table can include a Gradient Boosting classifier, for example, where the table is a complex text code. The TPI master matching tables can each contain a large complex number of rules as well as weights and WOE parameters for predictors. For example, where a given region or company is correlated with the company, in one embodiment, the TPI engine can employ a Random Forest algorithm, which is used for 70% of regions identified, where the remaining 30% of regions employ a Scorecard algorithm classifier because of data quality and data coverage. Thus, the system is configured to use AI to determine which classifiers fit to country/region data for the most accurate determination of likelihood that the company is a TPI. Based on data availability for this Mexico company ABC, random forest and unsupervised machine learning methods can be determined and selected in block  612 . 
     At block  614 , in at least one of the various embodiments, the TPI engine generates a TIP likelihood rating score from the TPI master matching table analysis. For example, based on TPI master match table of the TPI engine, which is the result of both supervised and unsupervised machine learning methods, the TPI Engine generates a TPI likelihood score of “5” on a 1-5 scale to ABC company, indicating there is a maximal likelihood that ABC company is a TPI. 
     At block  616 , in at least one of the various embodiments, the TPI engine generates and stores the TPI likelihood rating and then passes the rating to a scoring module for generating a TPI risk compliance rating from the TPI likelihood rating score and the general compliance risk rating score at block  634 . 
     At block  620 , in at least one of the various embodiments, a GCRM Engine also receives at least the company name for the request received at block  602 . For example, the GCRM Engine receives a request for analysis of the ABC company to generate a compliance risk assessment rating for ABC Company. 
     At block  622  the GCRM engine receives business entity data from a global business partner/3d party database(s)  320 , a business entity information database(s)  318 , and/or public data database(s)  316 . For example, in an embodiment, the GCRM engine receives a large database of information for processing, for example, business analysis scores and indicators (e.g.: Country Risk info, country/market, “Perceived Corruption Index,” company financial statements (5 years), trade/payment/delinquency information, and other risk indicator information from various data sources, such as social/mass media adverse coverage, previous compliance violations/warnings/sanctions/investigations instances and stage of investigations. As described herein, the GCRM Engine has an independent processing system, for example a massive big data processing architecture such as a HADOOP system or other mass data processor, in order to process data independently from the TPI engine for, inter alia, parallel processing with the TPI Engine. The GCRM Engine can be configured for committed big data processing such that the GCRM Engine, databases, and AI master matching table can take in the larger complex data set for optimal risk assessment. 
     At block  624 , in at least one of the various embodiments, the information for the identified business entity is correlated with the business information from at least one of the databases. For example, global partner or third party database(s) in or for Mexico provide local or regional data on the ABC company. In an embodiment, the system can determine that in local language, the business name contains words “Construction,” “Transportation,” and “Consulting.” In an embodiment, data from global business partners can be run though, for example, machine translation and name recognition classifiers for processing by the GCRM engine. The GCRM engine can also receive public data from public sources  116 , such as Perceived Corruption Index by country, social media data, or judicial information. 
     At block  626 , in at least one of the various embodiments, GCRM identifies one or more risk factors for the identified business entity. For example, the GCRM can identify that Mexico has index level at 30 on the Perceived Corruption Index, ranked 123 out of 176 countries/markets (relatively high end). The GCRM may also identify certain industries, such as construction, as more subject to compliance risk. The lack of public derogatory information is found about ABC company on social media can also be a factor that lowers risk, and at the same time, indictment of kickbacks charge on one of ABC&#39;s partners and 2 write-offs in ABC&#39;s payment accounts in last 5 years increase ABC&#39;s risk score. 
     At block  628 , in at least one of the various embodiments, the GCRM engine processes the business entity identification data, correlated business information, and/or the identification data using the master matching table and processing module. In an embodiment, the master matching table includes one or more machine learning AI engine classifiers. The classifier can in be selected from a supervised machine learning engine, an unsupervised machine learning engine, or both. For example, the GCRM engine&#39;s master matching table can include a Random Forest and SVM (Support Vector Machine), for example for binary file table (for machine read only). For another example, the GCRM engine&#39;s master matching table can include a Gradient Boosting classifier, for example, where the table is a complex text code. The GCRM master matching tables can each contain a large complex number of rules as well as weights for predictors. In one embodiment, the GCRM engine can employ a Random Forest algorithm, which is used for 70% of regions identified, where the remaining 30% of regions are split between SVM and Gradient Boosting classifier algorithms. In at least one of the various embodiments, countries are grouped into regions, and each region is run on different classifiers as described above. Thus, the system is configured to use AI to determine which classifiers fit to country/region data for the most accurate determination of the compliance risk. 
     At block  628 , in at least one of the various embodiments, the GCRM engine generates a GCRM rating score from the master matching table analysis. For example, based on GCRM Master Match Table of the GCRM engine, which is the result of both supervised and unsupervised machine learning methods, the GCRM Engine generates a compliance risk rating of “E” on a A-E scale for ABC company, indicating there is a maximal likelihood that ABC company is at high risk for corruption or bribery. 
     At block  630 , in at least one of the various embodiments, the GCRM engine stores the GCRM likelihood rating and then passes the rating to a scoring module for generating a TPI risk compliance rating from the TPI likelihood rating score and the general compliance risk rating score at block  634 . 
     At block  634 , the system then takes TPI likelihood rating score and the general compliance risk rating score and generates a final TPI likelihood and risk rating. For example, ABC company is given a TPI likelihood and risk rating of “5E”, meaning ABC is very likely a TPI and is also at very high risk of being a compliance risk. As ABC does not self-report as a TPI or has not otherwise been identified as such, and as ABC has been found via data AI processing to be at high risk of corruption, the system provides a tool for identifying and mitigating ABAC that could not previously be done, as prior art systems relied on absent self-reporting of TPIs. 
     It will be understood that each block of the flowchart illustration, and combinations of blocks in the flowchart illustration, can be implemented by computer program instructions. These program instructions can be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart block or blocks. The computer program instructions can be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer-implemented process such that the instructions, which execute on the processor to provide steps for implementing the actions specified in the flowchart block or blocks. The computer program instructions can also cause at least some of the operational steps shown in the blocks of the flowchart to be performed in parallel, for example as described herein. Moreover, some of the steps can also be performed across more than one processor, such as might arise in a multi-processor computer system or even a group of multiple computer systems. In addition, one or more blocks or combinations of blocks in the flowchart illustration can also be performed concurrently with other blocks or combinations of blocks, or even in a different sequence than illustrated without departing from the scope or spirit of the invention. 
     Accordingly, blocks of the flowchart illustration support combinations of means for performing the specified actions, combinations of steps for performing the specified actions and program instruction means for performing the specified actions. It will also be understood that each block of the flowchart illustration, and combinations of blocks in the flowchart illustration, can be implemented by special purpose hardware-based systems, which perform the specified actions or steps, or combinations of special purpose hardware and computer instructions. The foregoing example should not be construed as limiting and/or exhaustive, but rather, an illustrative use case to show an implementation of at least one of the various embodiments. 
     In at least one of the various embodiments the system can be configured to send data to client device. Such user interfaces can have more or fewer user interface elements which can be arranged in various ways. In some embodiments, user interfaces can be generated using web pages, mobile applications, emails, PDF documents, text messages, or can include processes and/or API&#39;s for generating user interfaces. In an embodiment, the system can be configured to provide a client application with one-click batch processing to batch process a list of business entities to generate a TPI identification risk compliance ratings for each of the listed business entities.