DUAL ARTIFICIAL INTELLIGENCE SYSTEM FOR REAL-TIME BENCHMARKING AND PREDICTIVE MODELING

Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to provide real-time benchmarking of an entity account. As such, the system allows for use of a machine learning engine to collect information from a plurality of sources and predict future account behavior associated with said sources. A single third party entity may lack enough historical data for accurate predictive modeling. By collecting data associated with a plurality of third party entities, the system may more accurately identify data trends and generate predictions of future account behavior. Thus, the system may benefit a number of entities, by providing real-time data analysis that would not be obtainable by any one entity operating alone.

BACKGROUND

In a distributed network where a managing entity is connected to multiple secondary entities, the secondary entities are often unable to communicate with or access data associated with each other. This prevents each secondary entity from performing a comparative analysis against the other entities. Secondary entities may also struggle to create predictive models due to a lack of available historical data. As such, a need exists for a system which is able to provide real-time benchmarking of any secondary entity within a group of secondary entities, as well as a system for predictive modeling using shared historical data across the group.

BRIEF SUMMARY

Embodiments of the invention relate to systems, methods, and computer program products for benchmarking and predictive modeling, the invention including: receive a plurality of unique data packets from one or more third party entities, wherein each unique data packet comprises data associated with an entity account associated with the one or more third party entities; determine, from the resource transfer dataset, a set of standard characteristics of the unique date packet; query a database for one or more datasets matching the set of standard characteristics and append the unique data packet to the one or more datasets matching the set of standard characteristics, creating a combined dataset; and process the combined dataset via a machine learning engine to predict one or more future behaviors of the entity account.

In some embodiments, the invention further comprises transmitting a notification to a first third party entity system, wherein the notification comprises information associated with the one or more predicted future behaviors.

In some embodiments, the invention further comprises automatically causing the first third party entity system to transmit the information associated with the one or more predicted future behaviors to a second third party system.

In some embodiments, the invention further comprises determining, via the machine learning engine, one or more adjustments associated with a predetermined preferred behavior of the one or more third party entities.

In some embodiments, the invention further comprises calculating, via the machine learning engine, a confidence degree associated with the one or more adjustments, wherein the confidence degree of each adjustment is a probability of the adjustment increasing an overall volume of unique data packets received from the one or more third party systems.

In some embodiments, the invention further comprises generating a weighted list of the one or more adjustments, wherein the weighted list is sorted according to the confidence degree.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

“Entity” or as used herein may refer to any organization, entity, or the like which employs information technology resources and particularly technology infrastructure configured for processing large amounts of data. This data can be related to the people who work for the entity, its products or services, the customers, vendors, or any other aspect of the operations of the entity. As such, the entity may be any institution, group, association, establishment, authority, or the like, employing information technology resources for processing large amounts of data.

“Managing entity” as used herein may refer to any organization, entity, or the like in the business of moving, investing, or lending money, dealing in financial instruments, or providing financial services. This may include commercial banks, thrifts, federal and state savings banks, savings and loan associations, credit unions, investment companies, insurance companies and the like. In some embodiments, the managing entity may allow a secondary entity or a user to establish an account with the managing entity. An “account” may be the relationship that the secondary entity or user has with the managing entity. Examples of accounts include a deposit account, such as a transactional account (e.g., a banking account), a savings account, an investment account, a money market account, a time deposit, a demand deposit, a pre-paid account, a credit account, or the like. The account is associated with and/or maintained by the managing entity. In other embodiments, a managing entity may not be a financial institution. In still other embodiments, the managing entity may be a merchant itself.

“Entity system” or “managing entity system” as used herein may refer to the computing systems, devices, software, applications, communications hardware, and/or other resources used by the entity to perform the functions as described herein. Accordingly, the entity system may comprise desktop computers, laptop computers, servers, Internet-of-Things (“IoT”) devices, networked terminals, mobile smartphones, smart devices (e.g., smart watches), network connections, and/or other types of computing systems or devices and/or peripherals along with their associated applications.

“User” as used herein may refer to an individual associated with an entity. As such, in some embodiments, the user may be an individual having past relationships, current relationships or potential future relationships with an entity. In some instances, a “user” is an individual who has a relationship with the entity, such as a customer or a prospective customer. Accordingly, as used herein the term “user device” or “mobile device” may refer to mobile phones, personal computing devices, tablet computers, wearable devices, and/or any portable electronic device capable of receiving and/or storing data therein and are owned, operated, or managed by a user.

“Transaction” or “resource transfer” as used herein may refer to any communication between a user and a third party merchant or individual to transfer funds for purchasing or selling of a product. A transaction may refer to a purchase of goods or services, a return of goods or services, a payment transaction, a credit transaction, or other interaction involving a user's account. In the context of a financial institution, a transaction may refer to one or more of: a sale of goods and/or services, initiating an automated teller machine (ATM) or online banking session, an account balance inquiry, a rewards transfer, an account money transfer or withdrawal, opening a bank application on a user's computer or mobile device, a user accessing their e-wallet, or any other interaction involving the user and/or the user's device that is detectable by the financial institution. A transaction may include one or more of the following: renting, selling, and/or leasing goods and/or services (e.g., groceries, stamps, tickets, DVDs, vending machine items, and the like); making payments to creditors (e.g., paying monthly bills; paying federal, state, and/or local taxes; and the like); sending remittances; loading money onto stored value cards (SVCs) and/or prepaid cards; donating to charities; and/or the like.

The system allows for use of a machine learning engine to collect a variety of historical information from a plurality of secondary entity systems and predict future performance associated with said secondary entity systems. Each secondary entity may only have access to a limited amount of historical data. By collecting data associated with each secondary entity, the system may identify data trends, generate predictions of future performance, and provide a comparative benchmarking analysis. In this way, the system may benefit a number of secondary entities, by providing real-time insights and data analysis that would not be obtainable by any one entity operating alone. Additionally, the features and functions of the system may provide a managing entity with real-time suggestions of actions that may affect future performance.

FIG.1illustrates an operating environment100for the benchmarking and modeling system, in accordance with one embodiment of the present disclosure. As illustrated, the operating environment100may comprise a primary managing entity system500and one or more third party systems400(e.g., secondary entity systems) in operative communication with one or more user device(s)104associated with one or more user(s)102. The operative communication may occur via a network101as depicted, or the user(s)102may be physically present at a location associated with the primary managing entity system500and/or third party system(s)400, such as a computer terminal or point-of-sale device located within a storefront. The operating environment also includes a benchmarking and modeling system200, a database300, and/or other systems/devices not illustrated herein and connected via a network101. As such, the user102may complete a resource transfer via the managing entity system500or the third party system(s)400by establishing operative communication channels between the user device104and the managing entity system500or third party system400via a wireless network101. In other embodiments, the user may complete a resource transfer via the managing entity system500or the third party system(s)400by interfacing directly with either system. In some embodiments, the third party system(s)400may transmit/receive data other than resource transfer information to/from the managing entity system300.

Typically, the benchmarking and modeling system200and the database300may be in operative communication with the managing entity system500and third-party system(s)400, via the network101, which may be the internet, an intranet or the like. InFIG.1, the network101may include a local area network (LAN), a wide area network (WAN), a global area network (GAN), and/or near field communication (NFC) network. The network101may provide for wireline, wireless, or a combination of wireline and wireless communication between devices in the network. In some embodiments, the network101includes the Internet. In some embodiments, the network101may include a wireless telephone network. Furthermore, the network101may comprise wireless communication networks to establish wireless communication channels such as a contactless communication channel and a near field communication (NFC) channel (for example, in the instances where communication channels are established between the user device104and the managing entity system500or third party system400). In this regard, the wireless communication channel may further comprise near field communication (NFC), communication via radio waves, communication through the internet, communication via electromagnetic waves and the like.

The user device104may comprise a mobile communication device, such as a cellular telecommunications device (i.e., a smart phone or mobile phone), a computing device such as a laptop computer, a personal digital assistant (PDA), a mobile internet accessing device, or other mobile device including, but not limited to portable digital assistants (PDAs), pagers, mobile televisions, laptop computers, cameras, video recorders, audio/video player, radio, GPS devices, any combination of the aforementioned, or the like.

The managing entity system500may comprise a communication module and memory (not illustrated) and may be configured to establish operative communication channels with a third party system400and/or a user device104via a network101. The managing entity may comprise an entity data repository which stores entity account data. This data may be used by the managing entity to facilitate resource transfers between the third party systems400and a user102, user device104, other users, merchants, and/or other third-party systems400. In some embodiments, the managing entity system is in operative communication with the benchmarking and modeling system200and database300via a private communication channel. The private communication channel may be via a network101or the benchmarking and modeling system200and database300may be fully integrated within the managing entity system500.

As will be discussed in greater detail with respect toFIG.3andFIG.4, the managing entity system500and the third party system(s)400may communicate with the benchmarking and modeling system200in order to transmit data associated with resource transfers initiated by a plurality of users102. In some embodiments, the managing entity may utilize the features and functions of the benchmarking and modeling system to predict entity account behavior and anticipate future changes in a third party system. In other embodiments, the managing entity and/or the one or more third party systems may utilize the benchmarking and modeling system to react to identified trends and/or take preemption action related to upcoming benchmarks.

FIG.2illustrates a block diagram of the benchmarking and modeling system200associated with the operating environment100, in accordance with embodiments of the present invention. As illustrated inFIG.2, the benchmarking and modeling system200may include a communication device244, a processing device242, and a memory device250having an analysis engine253, a processing system application254and a processing system datastore255stored therein. As shown, the processing device242is operatively connected to and is configured to control and cause the communication device244, and the memory device250to perform one or more functions. In some embodiments, the analysis engine253and/or the processing system application254comprises computer readable instructions that when executed by the processing device242cause the processing device242to perform one or more functions and/or transmit control instructions to the database300, the managing entity system500, and/or the communication device244. It will be understood that the analysis engine253and/or the processing system application254may be executable to initiate, perform, complete, and/or facilitate one or more portions of any embodiments described and/or contemplated herein. The analysis engine253may comprise executable instructions associated with data processing and analysis related to resource transfer data and may be embodied within the processing system application254in some instances. The benchmarking and modeling system200may be owned by, operated by and/or affiliated with the same managing entity that owns or operates the managing entity system500. In some embodiments, the benchmarking and modeling system200is fully integrated within the managing entity system500.

The analysis engine253may further comprise a data analysis module260, a machine learning engine261, and a machine learning dataset(s)262. The data analysis module260may store instructions and/or data that may cause or enable the benchmarking and modeling system200to receive, store, and/or analyze data received by the managing entity system500, the database300, and the one or more third-party system(s)400. The data analysis module may process data to identify account characteristics as is discussed in greater detail with respect toFIG.3. The machine learning engine261and machine learning dataset(s)262may store instructions and/or data that cause or enable the benchmarking and modeling system200to determine, in real-time and based on received information, a predicted behavior of an entity account associated with a third party system400over a particular period of time. The machine learning dataset(s)262may contain data queried from database300and/or may be based on historical data relating to third party systems, users, past resource transfers, location, specific characteristics of third party systems, and/or the like. In some embodiments, the machine learning dataset(s)262may also contain data relating to entity activity other than resource transfers as is further described herein.

The machine learning engine261may receive data from a plurality of sources and, using one or more machine learning algorithms, may generate one or more machine learning datasets262. Various machine learning algorithms may be used without departing from the invention, such as supervised learning algorithms, unsupervised learning algorithms, regression algorithms (e.g., linear regression, logistic regression, and the like), instance based algorithms (e.g., learning vector quantization, locally weighted learning, and the like), regularization algorithms (e.g., ridge regression, least-angle regression, and the like), decision tree algorithms, Bayesian algorithms, clustering algorithms, artificial neural network algorithms, and the like. Additional or alternative machine learning algorithms may be used without departing from the invention.

The machine learning datasets262may include machine learning data linking one or more details of an entity account (e.g. resource transfer volume/amount, additional costs associated with transfers, user information, recipient information, date/time information, tax information, and/or the like) with similar entity accounts to identify one or more patterns or sequences of account behavior that may aid in predicting one or more future changes by the same entity or third party system or by another entity with a similar account history. For instance, the machine learning datasets262may include data linking a series of historical resource transfers at particular dates/times with a likelihood of users initiating subsequent, similar, transfers at a predicted future date/time. Thus, this data may enable to the benchmarking and modeling system200to predict a likely future increase or decrease in the entity account. Data associated with a resource transfer may be supplemented by additional data obtained from an interaction between the user device104and the managing entity system500or third party system(s)400. For example, in some embodiments, the system may determine, based on data obtained from a plurality of user devices104, that a user is a likely to interact with a particular third party system based on a having downloaded a software application specific to that third party system. The benchmarking and modeling system200may weight that information accordingly to predict an increase in an entity account's activity based on a third party system associated with the account launching a software application. Additionally or alternatively, the system may determine, based on a plurality of historical resource transfer data, that a volume of transfers with an entity account is likely to increase during certain months of the year. The benchmarking and modeling system200may weight that information accordingly to predict that similar entity accounts will experience similar increases in transfer volume during those months.

The communication device244may generally include a modem, server, transceiver, and/or other devices for communicating with other devices on the network101. The communication device244may be a communication interface having one or more communication devices configured to communicate with one or more other devices on the network101, such as the transfer volume control system200, the user device(s)104, other processing systems, data systems, etc.

Additionally, referring to the benchmarking and modeling system200illustrated inFIG.2, the processing device242may generally refer to a device or combination of devices having circuitry used for implementing the communication and/or logic functions of the transfer volume control system200. For example, the processing device242may include a control unit, a digital signal processor device, a microprocessor device, and various analog-to-digital converters, digital-to-analog converters, and other support circuits and/or combinations of the foregoing. Control and signal processing functions of the benchmarking and modeling system200may be allocated between these processing devices according to their respective capabilities. The processing device242may further include functionality to operate one or more software programs based on computer-executable program code252thereof, which may be stored in a memory device250, such as the processing system application254and the analysis engine253. As the phrase is used herein, a processing device may be “configured to” perform a certain function in a variety of ways, including, for example, by having one or more general-purpose circuits perform the function by executing particular computer-executable program code embodied in computer-readable medium, and/or by having one or more application-specific circuits perform the function. The processing device242may be configured to use the network communication interface of the communication device244to transmit and/or receive data and/or commands to and/or from the other devices/systems connected to the network101.

The memory device250within the benchmarking and modeling system200may generally refer to a device or combination of devices that store one or more forms of computer-readable media for storing data and/or computer-executable program code/instructions. For example, the memory device250may include any computer memory that provides an actual or virtual space to temporarily or permanently store data and/or commands provided to the processing device242when it carries out its functions described herein. The memory device250may comprise an entity data repository256. The entity data repository256may comprise at least one entity account datasets257, with each entity account dataset257being associated with a third party system400.

In some instances, various features and functions of the invention are described herein with respect to a “system.” In some instances, the system may refer to the benchmarking and modeling system200performing one or more steps described herein in conjunction with other devices and systems, either automatically based on executing computer readable instructions of the memory device250, or in response to receiving control instructions from the managing entity system500. In some instances, the system refers to the devices and systems on the operating environment100ofFIG.1. The features and functions of various embodiments of the invention are be described below in further detail.

It is understood that the servers, systems, and devices described herein illustrate one embodiment of the invention. It is further understood that one or more of the servers, systems, and devices can be combined in other embodiments and still function in the same or similar way as the embodiments described herein.

FIG.3is a high-level process flow diagram illustrating a process using the benchmarking and modeling system, in accordance with one embodiment of the present disclosure. The process begins at block600, where the system receives a data packet from a managing entity system500or a third party system400, wherein the data packet contains information characterizing a resource transfer. The resource transfer information contained within the data packet may include but is not limited to data such as time, location, description of a product/service, resource amount, resource instrument or account used to complete the transfer, any additional costs collected by a managing entity that executed the transfer, information identifying the transfer recipient, and/or information identifying a user that initiated the transfer. In some embodiments the system may receive a unique data packet after each occurrence of an individual transfer, or in other embodiments the third party may choose to group data packets together and transfer the information after a predetermined amount of time, such as once per day. In some embodiments, a unique data packet may comprise additional information about a third party entity (e.g. size of the entity, location, technology capabilities, tax information, and/or the like). In some embodiments, the system may simultaneously receive data from a plurality of third party systems. Additionally or alternatively, the system may receive unique data packets from a managing entity system, where the managing entity system has received and consolidated data from the plurality of third-party systems.

The process may then continue to block610, wherein for each unique data packet, the system determines a set of standard characteristics from the information associated with the resource transfer or the information associated with the third party entity (e.g., via the data analysis module260). Standard characteristics may include any type of information included in a received data packet and may be normalized, via the data analysis module, depending on the specific formatting used by each third party entity that transmits data. For example, in some embodiments, a first third party system may include in each data packet a percentage amount of a resource transfer total that was collected as an additional cost. A second third party system may include in each data packet a dollar amount that was collected as an additional cost. Thus, the data analysis module260may convert the dollar amounts received from the second entity into percentages in order to create a standard characteristic of “additional cost collected.” In some embodiments, standard characteristics such as an “entity size” category (e.g. large entity, small entity, individual, etc.) may be assigned based on a calculated similarity score to one of a plurality of predetermined categories.

The process may then continue to block620, wherein the system may query the database300for datasets with similar standard characteristics as the newly received unique data packet. In some embodiments, the system may query for a larger selection of information, such as all resource transfers within a particular geographic area, within a particular date range, or the like. The system may then append the unique data packet to the queried data630and process the combined data via the machine learning engine261.

In block640ofFIG.3, the output of the machine learning engine is a newly generated machine learning dataset262. As previously described, the newly generated machine learning dataset may be used to link one or more details of a unique data packet (e.g. transfer amount, additional costs associated with the transfer, user information, recipient information, date/time information, and/or the like) with behavior of an entity account. This data may enable the system to identify one or more patterns or sequences of information, then predict a likely future behavior of an entity account as shown in block650. In block660ofFIG.3, the system may transmit a notification to the managing entity system, wherein the notification may contain predictions generated by the benchmarking and modeling system200. In some embodiments, the system may transmit this notification in the form of a regularly generated report. Additionally, or alternatively, the system may transmit this notification in response to a query from the managing entity or a third party system.

FIG.4is a high-level process flow diagram illustrating a process using the benchmarking and modeling system, in accordance with another embodiment of the present disclosure. The process begins at block700, wherein the system predicts a future entity account behavior. In some embodiments, the system may predict a future time at which an entity account will hit a specific benchmark (e.g. change in entity size, change in specific costs associated with the entity account, etc.). As previously discussed, the system may utilize the machine learning engine261to determine that an entity account will reach a particular benchmark based on historical data of a plurality of users, historical data of a plurality of third party systems, historical data of similar resource transfers, and/or the like. In some embodiments, the system may be configured to assign weights to particular datasets based on a calculated similarity score of the dataset, such that datasets with a higher degree of confidence may factor more heavily in the machine learning algorithms used. The system may then use the analysis engine253to determine one or more adjustments that, if executed by the entity associated with the entity account, would cause a desired behavior of the entity account. In some embodiments, the system may utilize the machine learning engine261to calculate the degree to which each potential adjustment may influence a volume of resource transfers and then weight a list of adjustments according to the probability of each adjustment increasing the overall volume of resource transfers involving the third party system.

The process continues in block720, wherein the system may generate a notification or data packet containing details of the predicted benchmarks, as well as the list of potential adjustments determined in block710. The message may contain information such as predicted times and/or dates of the benchmarks and/or the like. The process is completed in block730, wherein the system transmits that notification to the managing entity system or third party system. Additionally or alternatively, in embodiments wherein the system is fully integrated into the managing entity system, the system may automatically cause the managing entity system to transmit the list of adjustments to the third party system.

As will be appreciated by one of ordinary skill in the art, the present invention may be embodied as an apparatus (including, for example, a system, a machine, a device, a computer program product, and/or the like), as a method (including, for example, a business process, a computer-implemented process, and/or the like), or as any combination of the foregoing. Accordingly, embodiments of the present invention may take the form of an entirely software embodiment (including firmware, resident software, micro-code, and the like), an entirely hardware embodiment, or an embodiment combining software and hardware aspects that may generally be referred to herein as a “system.” Furthermore, embodiments of the present invention may take the form of a computer program product that includes a computer-readable storage medium having computer-executable program code portions stored therein.