UTILIZING DYNAMIC DATA SAMPLING TO IMPLEMENT MACHINE LEARNING MODELS FOR PROJECTING RECURRENCE OF PERIODIC EVENTS

The present disclosure relates to systems, non-transitory computer-readable media, and methods for projecting recurrence of periodic events. For example, embodiments of the disclosed systems determine that a target account qualifies for an account definition based on an account event occurring within a defined threshold time period, the account event being a periodic event of an unknown frequency. In one or more embodiments, based on determining the target account qualified for the account definition, the disclosed systems identify a set of features associated with the target account and based on the set of features, utilize a recurrence prediction machine learning model to generate a recurrence score that indicates a likelihood that the account event will repeat within the target account.

BACKGROUND

Recent years have seen significant improvements in conventional systems for training and implementing machine learning models. For example, conventional systems utilize a variety of machine learning models with learned parameters trained to perform various prediction tasks. To illustrate, conventional systems utilize decision tree or neural network machine learning approaches to generate predictions or classifications regarding network accounts, clients, client devices, or related events. For instance, some conventional systems utilize machine learning models to estimate the likelihood that an account event will take place or repeat within a network account. Although these conventional systems are able to generate and utilize predictive machine learning models, they have a number of technical deficiencies in relation to accuracy, efficiency, and flexibility of implementing computing devices.

For instance, conventional systems often utilize machine learning models that generate inaccurate predictive results. In particular, ground truth data is often difficult to obtain for models that predict the occurrence and/or recurrence of typically periodic events for which the event frequency is unknown. In addition, conventional systems are often inefficient, frequently leading to an inefficient utilization of computer resources, particularly when training models with extensive amounts of historical data. Also, conventional systems are often inflexible and rigid. For instance, as mentioned above, conventional systems that utilize machine learning models are often limited in scope to projecting events for which records of historical patterns are readily available.

These along with additional problems and issues exist with regard to conventional account classification or event prediction systems.

BRIEF SUMMARY

Embodiments of the present disclosure provide benefits and/or solve one or more of the foregoing or other problems in the art with systems, non-transitory computer-readable media, and methods for projecting recurrence of events within network accounts. In particular, the disclosed systems utilize recurrence prediction machine learning models to predict the likelihood of whether a periodic event of unknown frequency will repeat. For example, in one or more implementations the disclosed systems detect that an account event has occurred within a target account, determine a set of features associated with the target account, and utilize a recurrence prediction machine learning model to generate a recurrence score that indicates a likelihood that the account event will repeat within the target account. In one or more embodiments, the disclosed systems train a recurrence prediction machine learning model to predict recurrences of periodic events utilizing historical data corresponding to a sample population of accounts by sampling the historical data according to one or more assumed frequencies for the periodic event. In addition, in some embodiments, the disclosed systems utilize a recurrence incentive model to determine actions, based on recurrence scores generated by a recurrence prediction machine learning model, for increasing (or decreasing) the likelihood that an event will repeat. Accordingly, the disclosed systems improve the accuracy, efficiency, and flexibility of predictive machine learning models and implementing computing devices.

DETAILED DESCRIPTION

This disclosure describes one or more embodiments of an event recurrence projection system that predicts a likelihood of recurrence for periodic events of unknown frequency. For example, embodiments of the event recurrence projection system utilize a recurrence prediction machine learning model to generate recurrence scores indicating a likelihood that an event will repeat within target accounts. Model inputs can include various account features derived from data provided by a variety of sources. In addition, some embodiments of the disclosed systems utilize a recurrence incentive model to determine actions, based on generated recurrence scores, for increasing (or decreasing) the likelihood that an event will repeat within target accounts. In one or more embodiments, the disclosed systems train a recurrence prediction machine learning model based on historical data sampled according to one or more assumed values of an unknown frequency for periodic events. In this manner, the disclosed systems improve the accuracy, efficiency, and flexibility of computing devices in training and implementing predictive machine learning models.

In some embodiments, for example, the event recurrence projection system determines that a target account qualifies for an account definition based on an account event occurring within a defined threshold time period, the account event being a periodic event of an unknown frequency. In response to determining the account qualified for the account definition, the event recurrence projection system identifies a set of features associated with the target account and utilizes a recurrence prediction machine learning model to generate a recurrence score, based on the set of features, that indicates a likelihood that the account event will repeat within the target account.

Moreover, in one or more embodiments, the event recurrence projection system trains a recurrence prediction machine learning model utilizing historical data corresponding to a sample population of accounts by sampling the historical data according to a dynamic prediction window, based on one or more assumed values of the unknown frequency. In addition, in some embodiments, the event recurrence projection system utilizes a recurrence incentive model to determine one or more actions, based on a recurrence score and/or one or more features from an associated set of features, for increasing (or decreasing) the likelihood that the account event will repeat within the target account.

The event recurrence projection system provides many advantages and benefits over conventional systems and methods. For example, by utilizing a dynamic prediction window to train a recurrence prediction machine learning model, the event recurrence projection system improves accuracy relative to conventional systems. Specifically, embodiments of the event recurrence projection system utilize a dynamic prediction window corresponding to one or more assumed values of an unknown frequency of periodic events to sample historical data associated with a sample population of accounts. Embodiments of the event recurrence projection system utilize the dynamically sampled historical data to train a recurrence prediction machine learning model that can predict recurrence of unknown periodic events with increased accuracy compared to conventional predictive models. In addition, by utilizing a recurrence incentive model to intelligently determine responsive actions for increasing (or decreasing) the likelihood that an account event will repeat within a target account, the event recurrence projection system improves the accuracy and effectiveness of such actions.

Furthermore, by utilizing a dynamic prediction window to train a recurrence prediction machine learning model, the event recurrence projection system also improves efficiency relative to conventional systems. In particular, embodiments of the event recurrence projection system efficiently and effectively sample historical data for model training in a manner that is most relevant to the prediction of periodic events of unknown frequency. Indeed, by parsing (i.e., sampling) ground truth data according to the disclosed embodiments, the disclosed systems can reduce training time and computing resources relative to conventional systems utilizing predictive machine learning models.

Moreover, by sampling historical data according to one or more assumed values of an unknown frequency for periodic events (i.e., utilizing a dynamic prediction window), the event recurrence projection system exhibits increased flexibility relative to conventional systems. For instance, embodiments of the event recurrence projection system can be implemented to project recurrence of periodic account events of virtually any unknown frequency. Also, the disclosed embodiments can be implemented in a variety of environments, such as but not limited to a variety of financial networks, information databases, online members-only associations, and so forth.

As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and advantages of the event recurrence projection system. Additional detail is now provided regarding the meaning of such terms. For example, as used herein, the terms “network account” and “account” refers to a computer environment or location with personalized digital access to a web application, a native application installed on a client device (e.g., a mobile application, a desktop application, a plug-in application, etc.), or a cloud-based application. In particular embodiments, a network account includes a financial payment account through which a user can initiate a network transaction (e.g., an electronic payment for goods or services) on a client device or with which another user can exchange tokens, currency, or data. Examples of a network account include a CHIMER account.

Also, as used herein, the term “account event” refers to an event, occurrence, or incident that is enacted by, enacted upon, perpetuated by, caused by, implemented within, or otherwise associated with a network account. In particular embodiments, account events are periodic events that generally occur according to a particular frequency, such as but not limited to, for example, once every 24 hours, 7 days, 14 days, one calendar month, one fiscal or calendar year, and so forth. As a non-limiting example, an account event can be a financial transaction, such as a loan payment, a direct deposit of funds, a scheduled transfer of funds, and so forth.

Relatedly, as used herein, the term “network transaction” refers to a transaction performed as part of a digital exchange of funds, tokens, currency, or data between accounts or other connections of a computing system. In particular embodiments, the network transaction can be a mobile check deposit (e.g., a digital request for executing a check that can transfer funds from a check maker account to a recipient account), a direct deposit of a paycheck, a peer-to-peer (P2P) transfer of funds (e.g., a digital request for executing a direct transfer of funds from a financial account of a requesting user to a financial account of associated with another user), a purchase by credit or debit, a withdrawal of cash, and so forth. Indeed, a network transaction can be implemented via a variety of client devices. In some embodiments, the network transaction may be a transaction with a merchant (e.g., a purchase transaction) in which a merchant or payee indicated on a transaction request corresponds to the recipient account.

As used herein, the term “account definition” refers to a designation or classification of a network account. For example, an account definition includes a designation that a particular account event has occurred at least once within a network account. In some cases, such designations are contingent on more than one characteristic, such as the occurrence of a particular event within a threshold period of time.

As used herein, the term “feature” refers to characteristics or attributes related to a network account or account event. In particular embodiments, a feature includes account-based characteristics associated with an account holder (i.e., user) and/or a computing device associated with the network account and/or the account holder (e.g., a user computing device utilized to request/create the network account and/or to perform account events, such as but not limited to network transactions). Additional non-limiting examples of features are discussed below in relation toFIGS.3and7A-7C.

As used herein, the term “machine learning model” refers to a computer algorithm or a collection of computer algorithms that can be trained and/or tuned based on inputs to approximate unknown functions. For example, a machine learning model can include a computer algorithm with branches, weights, or parameters that changed based on training data to improve for a particular task. Thus, a machine learning model can utilize one or more learning techniques to improve in accuracy and/or effectiveness. Example machine learning models include various types of decision trees (e.g., decision trees, random forests, gradient boosting models, and so forth), support vector machines, Bayesian networks, linear regressions, logistic regressions, random forest models, or neural networks (e.g., deep neural networks).

As used herein, the term “neural network” refers to a machine learning model of interconnected artificial neurons (e.g., organized in layers) that communicate and learn to approximate complex functions and generate outputs based on a plurality of inputs provided to the neural network. In some cases, a neural network refers to an algorithm (or set of algorithms) that implements deep learning techniques to model high-level abstractions in data. For example, a neural network includes a multi-layer perceptron, a transformer neural network, a convolutional neural network, a recurrent neural network (e.g., an LSTM), a graph neural network, or a generative adversarial neural network.

Additional detail regarding the event recurrence projection system will now be provided with reference to the figures. In particular,FIG.1illustrates a computing system environment for implementing an event recurrence projection system106in accordance with one or more embodiments. As shown inFIG.1, the environment includes server(s)102, client device108, a network110, and a data repository112. In some embodiments, the environment includes additional systems connected to the event recurrence projection system106, such as an account management system, a security system, and so forth. The server(s)102can include one or more computing devices (and a database) to implement the event recurrence projection system106. Additional description regarding the illustrated computing devices (e.g., the server(s)102, the client device108, and/or the data repository112) is provided below in relation toFIGS.9-10.

As shown inFIG.1, the event recurrence projection system106utilizes the network110to communicate with the client device108and/or the data repository112. The network110may comprise, for example, any network described in relation toFIGS.9-10below. For example, the server(s)102communicates with the client device108to provide and receive information pertaining to network accounts, account events, account features, and so forth.

As illustrated, the event recurrence projection system106evaluates data from various sources, such as the client device108and the data repository112, to project (i.e., predict and incentivize or disincentivize) recurrence of account events. Additional inputs to the event recurrence projection model include but are not limited to data from third-party applications, server-side data repositories, user-provided inputs from devices other than the client device108, and so forth.

As indicated byFIG.1, the client device108includes a client application. In many embodiments, the event recurrence projection system106communicates with the client device108through the client application to, for example, receive and provide information including event and feature data pertaining to a target account. In addition, the event recurrence projection system106can receive information from the data repository112, such as historical data associated with a sample population of accounts.

As indicated above, the event recurrence projection system106can provide (and/or cause the client device108to display or render) visual elements within a graphical user interface associated with the client application. For example, the event recurrence projection system106can provide a graphical user interface that includes a login screen and/or an option to request/create a new account. In some cases, the event recurrence projection system106provides user interface information for a user interface for performing enacting account events to be considered by the event recurrent projection system106. As described in greater detail below, in some embodiments, the event recurrence projection system106also utilizes the recurrence incentive model116to determine appropriate actions for incentivizing (or disincentivizing) recurrence of an account event. In some cases, for example, the event recurrence projection system106performs one or more actions, as determined by the recurrence incentive model116, through the client device108, such as but not limited to: providing an incentive to a user of the target account, providing information associated with the account definition to the user of the target account, or activating one or more application features associated with the target account.

AlthoughFIG.1illustrates the environment having a particular number and arrangement of components associated with the event recurrence projection system106, in some embodiments, the environment may include more or fewer components with varying configurations. For example, in some embodiments, the event recurrence projection system106can communicate directly with the client device108and/or the data repository112, bypassing the network110. Further, the event recurrence projection system106can include more network components communicatively coupled together.

As discussed above, the event recurrence projection system106can generate a recurrence score based on account features of a target account and implement one or more responsive actions to increase (or decrease) the likelihood that an account event will repeat within the target account. For instance,FIG.2illustrates the event recurrence projection system106utilizing a recurrence prediction machine learning model208to generate a recurrence score210based on account features206associated with a network account204(i.e., a target account). In addition,FIG.2shows the event recurrence projection system106utilizing a recurrence incentive model212to determine and/or implement one or more responsive actions214.

As illustrated, an inter-network facilitation system202includes (or monitors) the network account204for account events. For example, the inter-network facilitation system202can include but is not limited to a network management system, such as a database, a financial network, a membership network, a content management network, and so forth. In addition, as illustrated, the inter-network facilitation system202includes (or has access to) the account features206associated with the network account204.

Accordingly, based on the account features206, the event recurrence projection system106utilizes the recurrence prediction machine learning model208to generate the recurrence score210. As stated previously, the recurrence score210indicates a likelihood that a detected account event will repeat within the network account204. As discussed in greater detail below (e.g., in relation toFIG.5), in some embodiments the recurrence prediction machine learning model208is trained utilizing historical data as ground truth data, including event histories of sample accounts and corresponding historical records of account features.

As further illustrated inFIG.2, the recurrence incentive model212determines one or more responsive actions214, based on the recurrence score210, for increasing (or decreasing) the likelihood that the account event will repeat within the network account204. For example, if the recurrence score210is below (or above) a threshold recurrence score (or within a predetermined range of recurrence scores), then the recurrence incentive model212may determine that a specific responsive action214should be taken to incentivize (or disincentivize) or otherwise influence the reoccurrence of the account event. In some embodiments, the recurrence incentive model212also considers the account features206when determining responsive action(s)214. For example, in one or more embodiments, the recurrence prediction machine learning model208provides input to the recurrence incentive model212, such as identification of which account features206affected the recurrence score210with greater magnitude relative to other features. Accordingly, in some embodiments, the recurrence prediction machine learning model208and the recurrence incentive model212operate in cooperation to determine effective responsive action(s)214for increasing (or decreasing) the likelihood that an account event will repeat within the network account204.

As mentioned above, responsive action(s)214can include a variety of actions configured to increase or decrease the likelihood of an account event reoccurring within the network account204, thus increasing or decreasing the recurrence score210associated with that event. Accordingly, in some embodiments, an increasing recurrence score210is indicative of an increasing likelihood of event recurrence, whereas a decreasing recurrence score210is indicative of a decreasing likelihood of event recurrence. In one or more embodiments, for example, recurrence score210approaches 1.0 as the likelihood of recurrence increases. As a non-limiting example, the responsive action(s)214can include notifying a user of the network account204of one or more actions for increasing the likelihood that the account even will repeat within the network account204. Additionally or alternatively, the responsive action(s)214can include one or more of providing an incentive to a user of the target account, providing information associated with the account definition to the user of the target account, or activating one or more application features associated with the target account. Additional responsive actions are discussed below in relation toFIG.4.

As mentioned previously, the event recurrence projection system106can utilize a recurrence prediction machine learning model to generate a recurrence score based on a set of features associated with a target account. For example,FIG.3includes an exemplary list of account features that the event recurrence projection system106can utilize as inputs for a recurrence prediction machine learning model to generate recurrence scores. The account features listed are given as non-limiting example. As such, embodiments can include more or fewer account features than included inFIG.3, as well as account features not listed inFIG.3.

For example, as listed inFIG.3, account events can include but are not limited to: whether the detected account event is a first occurrence for the target account, the source or origin of the account event, the number days passed since the detected account event, the relative magnitude of the account event, and so forth. Account resources can include but are not limited to the availability of resources related to the account event, such as account funds, memory, storage, computation resources, bandwidth, personnel, materials, or virtually any quantifiable resource determined to be relevant to the recurrence of the account event.

Additional account features listed include account activity, account usage, security events associated with the target account, account correspondence (e.g., correspondence between the account with other accounts, correspondence between the account owner and the account provider, etc.), enrollment data corresponding to the target account, attributes of the account user(s), data associated with third-party accounts related to the target account, and other metrics as found relevant to the account event.

As mentioned previously, the event recurrence projection system106can utilize a recurrence prediction machine learning model comprising multiple models specifically trained for varying circumstances, such as differences in event frequency and/or other account definitions. For example,FIG.4illustrates the event recurrence projection system106utilizing a recurrence prediction machine learning model408comprising a first prediction model408aand a second prediction model408bbased on whether a detected account event is a first or subsequent occurrence within a target account.

As illustrated inFIG.4, the event recurrence projection system106detects an account event at402and determines that the target account qualifies for an account definition at404. For example, in some embodiments, the target account is designated with the account definition when the detected account event has occurred within a defined threshold time period. In some embodiments, for example, the event recurrence projection system106utilizes an account definition governed by a threshold time period to avoid monitoring and projecting recurrence of account events after a period of time upon which it is unlikely that the account event will reoccur. The threshold time period, for example, can be determined based on historical data comprising a record of event occurrences and reoccurrences within a sample population of accounts.

As further illustrated inFIG.4, in some embodiments the event recurrence projection system106utilizes a recurrence prediction machine learning model408comprising two (or more) prediction models for determining a recurrence score410. For instance, separately trained prediction models408aand408bcan be implemented for differing circumstances, such as whether the account event detected at402was a first occurrence of the account event within the target account, as determined at406. As shown, when the detected event is a first occurrence for the target account, the event recurrence projection system106utilizes the prediction model408ato determine the recurrence score410. However, if the detected event is a subsequent occurrence (i.e., not the first occurrence) of the account event within the target account, the event recurrence projection system106utilizes the prediction model408bto determine the recurrence score410. Accordingly, predictive models of the recurrence prediction machine learning model408can be individually trained to increase accuracy of generated recurrence scores under particular circumstances. Alternatively, in some embodiments, the event recurrence projection system106can utilize a single machine learning model to generate recurrence scores under a variety of circumstances. For example, in some embodiments, the event recurrence projection system106trains the recurrence prediction machine learning model408to deduce particular circumstances from input data received (e.g., as a parameter of a decision tree or other model).

In addition, as shown inFIG.4, the event recurrence projection system106utilizes a recurrence incentive model412to determine one or more responsive actions based on recurrence score410. As mentioned previously, the recurrence incentive model412can also utilize additional inputs from the recurrence prediction machine learning model408to determine actions for increasing (or decreasing) the recurrence score410, such as but not limited to a designation by the recurrence prediction machine learning model408of which account features most affected the recurrence score410.

Also, in some embodiments, the recurrence incentive model412determines that the recurrence score410falls below a defined threshold score and, in response, notifies a user of the target account of one or more actions for increasing the likelihood that the account event (detected at402) will repeat within the target account. Similarly, in some embodiments, the recurrence incentive model412determines that the recurrence score410falls within a defined range of recurrence scores, such as one of a plurality of quantiles (e.g., one of five quintiles) representing a frequency distribution of recurrence scores within a population of accounts. Accordingly, in such embodiments, the event recurrence projection system106can utilize the recurrence incentive model412to determine actions for increasing (or decreasing) recurrence scores of account based on the quantile to which each respective account belongs.

Moreover, as shown inFIG.4, the recurrence incentive model412includes various exemplary responsive actions414a-e. However, the responsive actions414a-eshown are given as non-limiting examples, whereas embodiments can include any number of responsive actions for increasing or decreasing the likelihood that an account event will repeat within a target account.

For instance, the recurrence incentive model412can determine one or more actions414ato educate a user of the target account. For example, in some instances, user education with respect to causes, effects, rewards, and other factors associated with the account event detected at402can be effective in increasing (or decreasing) the likelihood that an account event will repeat within a target account.

Furthermore, in some instances, the recurrence incentive model412may determine to implement an action414bfor increasing user correspondence, such as but not limited to user correspondence with representatives of an account management system, with other users within the same system, or with a relevant third party.

Further still, in various circumstances, the recurrence incentive model412may determine to set forth one or more incentives to increase (or decrease) the likelihood that the account event will repeat within the target account. For example, responsive action414cincludes one or more event incentives targeted at incentivizing the account event itself (e.g., an offering or reward in response to reoccurrence of the account event). Responsive action414dincludes one or more incentives specifically targeted at the target account (e.g., an offering or reward for particular categories of account activity known to incentivize (or disincentivize) the account event). Responsive action414eincludes one or more incentives targeted at social media or other social interactions (e.g., an offering or reward to the user for interacting with and/or referring new users).

As mentioned previously, the event recurrence projection system106can utilize historical data corresponding to a sample population of accounts to train a recurrence prediction machine learning model. For example,FIG.5illustrates the event recurrence projection system106training a recurrence prediction machine learning model510utilizing past events506and a record of account features508corresponding to a sample account504of a sample population of accounts502.

Specifically, the sample population of accounts502comprises a plurality of real network accounts for which relevant historical data is available. As shown inFIG.5, the event recurrence projection system106utilizes known past events506and account features508corresponding to each sample account504of the sample population of accounts502to train the recurrence prediction machine learning model510to generate recurrence prediction scores for target accounts, the recurrence prediction score being indicative of a likelihood that an account event will repeat within each respective target account.

As illustrated, for a given sample event of the past events506, the event recurrence projection system106utilizes the recurrence prediction machine learning model510to generate a predicted recurrence score512based on account features from the record of account features508. For example, in certain implementations, the record of account features is sampled at various sample time frames relative to the sample event to determine account features for input to the recurrence prediction machine learning model510.

In response to generating the predicted recurrence score512, the event recurrence projection system106determines from the past events506whether the account event indeed did reoccur within the sample account504. In some embodiments, the event recurrence projection system106only considers events that reoccurred within a defined threshold time period. Thus, the past events506(i.e., as ground truth) are compared with the predicted recurrence score512and the event recurrence projection system106adjusts one or more parameters of the recurrence prediction machine learning model510to increase the accuracy of generated recurrence scores. Accordingly, the foregoing process may be repeated across multiple sample accounts while considering multiple recorded past events and account features, and in light of multiple prediction time frames and threshold time periods.

As mentioned previously, the event recurrence projection system106can utilize a dynamic prediction window to sample historical data for training a recurrence prediction machine learning model. For example,FIG.6illustrates the event recurrence projection system106utilizing a dynamic prediction window602to sample historic data, as represented by timeline600. In particular, the timeline600represents known historical data for a sample population of accounts, the latest date of which is illustrated as end date604. In some implementations, for example, end date604is the present day at which training of the model is implemented. In any case, the end date604is the final date for which validation data is available for training of the recurrence prediction machine learning model.

As shown inFIG.6, the timeline600is sampled according to a dynamic prediction window602. Specifically, the dynamic prediction window602is based on an assumed value of an unknown frequency for the account event for which the recurrence prediction machine learning model is trained to generate recurrence scores. In certain embodiments, for example, the event recurrence projection system106trains multiple recurrence prediction machine learning models, utilizing a different assumed frequency for each model. For example, for an assumed frequency of once every seven days, the dynamic prediction window602encompasses data within a seven-day window of timeline600. Accordingly, when a predicted recurrence score is generated for a particular date on timeline600, the ground truth date for that predicted recurrence score would be seven days later, at which the historical record would indicate whether the account event has repeated.

Indeed, in the illustrated embodiment, the dynamic prediction window602dictates the dates on timeline600from which a prediction is made and from which the same prediction is validated. Furthermore, the dynamic prediction window602can be adjusted according to event frequencies observed in historical data and/or a plurality of assumed values (e.g., seven days, fourteen days, one month, 32 days, or 90 days) can be implemented to train a recurrence prediction machine learning model (or multiple recurrence prediction machine learning models) according to one or more embodiments.

As further illustrated inFIG.6, the event recurrence projection system106utilizes the dynamic prediction window602to sample the timeline600at a plurality of dates (or any other time unit should the assumed value of unknown frequency not be in days) to train a recurrence prediction machine learning model. For instance, starting with end date604as a first validation date, the event recurrence projection system106samples account features at date606aaccording to the magnitude of the dynamic prediction window602. Accordingly, the recurrence prediction machine learning model generates a predicted recurrence score at date606abased on recorded account features at date606aand utilizes event occurrence data (i.e., whether the account event reoccurred) at end date604as ground truth for training the model.

In addition, the event recurrence projection system106shifts the dynamic prediction window602to sample the data at various moments in time. As shown, for example, another sample comprises a sample date606bwith a corresponding validation date608aat which the event recurrence projection system106trains the recurrence prediction machine learning model according to whether the event has reoccurred at the validation date608a. Similarly, yet another sample comprises a sample date606cwith a corresponding validation date608b. Accordingly, the timeline600is sampled according to the dynamic prediction window602until a prediction date606y, with corresponding validation date608x, that are the most prior sample date for which event and feature data is available. Indeed, the event recurrence projection system106implements the dynamic prediction window602to repeatedly sample the timeline600for a plurality of prediction and validation points for training of a recurrence prediction machine learning model.

To further illustrate, in one or more embodiments, the event recurrence projection system106can project recurrence of direct deposits or other network transactions within user accounts of a financial network. Accordingly, the event recurrence projection system106can project recurrence of direct deposits utilizing a recurrence prediction machine learning model trained on historic data associated with a sample population of sample accounts.

Indeed, upon detecting that a direct deposit has taken place within a target account, the event recurrence projection system106can generate recurrence scores indicating a likelihood that a target account will repeat a direct deposit within the target account. Furthermore, the event recurrence projection system106can utilize recurrence scores and/or account features to determine one or more responsive actions for increasing the likelihood that a direct deposit will reoccur within the target account.

As mentioned previously, the event recurrence projection system106can utilize a variety of account features as input for a recurrence prediction machine learning model and/or a recurrence incentive model. For instance,FIGS.7A-7Cinclude an exemplary list of account features that the event recurrence projection system106can utilize as inputs for a recurrence prediction machine learning model to generate recurrence scores related to direct deposit recurrence or recurrence of other network transactions within a target account of a financial network. Indeed, embodiments include additional features (or fewer features) for input into the recurrence prediction machine learning model to generate recurrence scores and determine responsive actions for increasing the likelihood that a direct deposit or other transaction will repeat within a target account.

FIGS.1-6and7A-7C, the corresponding text, and the examples provide a number of different methods, systems, devices, and non-transitory computer-readable media of the event recurrence projection system106. In addition to the foregoing, one or more embodiments can also be described in terms of flowcharts comprising acts for accomplishing a particular result, as shown inFIG.8.FIG.8may be performed with more or fewer acts. Further, the acts may be performed in differing orders. Additionally, the acts described herein may be repeated or performed in parallel with one another or parallel with different instances of the same or similar acts.

WhileFIG.8illustrates acts according to one embodiment, alternative embodiments may omit, add to, reorder, and/or modify any of the acts shown inFIG.8. The acts ofFIG.8can be performed as part of a method. Alternatively, a non-transitory computer-readable medium can comprise instructions that, when executed by one or more processors, cause a computing device to perform the acts ofFIG.8. In some embodiments, a system can perform the acts ofFIG.8. Additionally, the acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or other similar acts.

As shown,FIG.8illustrates an example series of acts800for projecting recurrence of account events within network accounts. For instance, the series of acts800can include an act802for detecting an account event. In particular, the act802can include determining that a target account qualifies for an account definition based on an account event occurring within a defined threshold time period, the account event being a periodic event of an unknown frequency.

As also shown inFIG.8, the series of acts800can include an act804for identifying account features. In particular, the act804can include identifying a set of features associated with the target account based on determining the target account qualified for the account definition. In some embodiments, the set of features associated with the target account comprises one or more of event data associated with the account event, availability of target account resources, target account activity data, security incidents associated with the target account, correspondence history of the target account, or target account age.

In addition, as shown inFIG.8, the series of acts800can include an act806for generating a recurrence score based on the account features. In particular, the act806can include utilizing a recurrence prediction machine learning model to generate a recurrence score, based on the set of features, that indicates a likelihood that the account event will repeat within the target account. In some embodiments, the recurrence prediction machine learning model comprises a first model configured to generate recurrence scores for target accounts having experienced one occurrence of the periodic event, and a second model configured to generate recurrence scores for target accounts having experienced two or more occurrences of the periodic event.

Additionally, the series of acts800can include an act for training the recurrence prediction machine learning model utilizing historical data corresponding to a sample population of accounts by sampling the historical data according to one or more assumed values of the unknown frequency. In one or more embodiments, the one or more assumed values comprise one or more of seven days, fourteen days, one month, 32 days, or 90 days. In some embodiments, training the recurrence prediction machine learning model is repeated for a plurality of assumed values of the unknown frequency.

Furthermore, in some embodiments, training the recurrence prediction machine learning model further comprises an act for determining, from a sample account history of a sample account of the sample population, a sample set of features associated with the sample account at a time after occurrence of a past account event, the time after occurrence corresponding to an assumed value of the unknown frequency. Also, in some embodiments, training the recurrence prediction machine learning model includes an act for, based on the sample set of features, utilizing the recurrence prediction machine learning model to generate a sample recurrence score for the sample account corresponding to the time after occurrence. In addition, in some embodiments, training the recurrence prediction machine learning model includes an act for, based on a comparison of the sample recurrence score and the sample account history, adjusting the one or more parameters of the recurrence prediction machine learning model.

In addition, in some embodiments, the series of acts800can include an act for determining that the recurrence score falls below a defined threshold score and, in response, notifying a user of the target account of one or more actions for increasing the likelihood that the account event will repeat within the target account. Additionally or alternatively, in some embodiments, the series of acts800can include an act for, in response to determining that the recurrence score falls below the defined threshold score or within a defined range of scores (e.g., within a quantile of a frequency distribution of recurrence scores for a population of accounts), performing one or more actions comprising at least one of: providing an incentive to a user of the target account, providing information associated with the account definition to the user of the target account, or activating one or more application features associated with the target account.

FIG.9illustrates a block diagram of an example computing device900that may be configured to perform one or more of the processes described above. In one or more embodiments, the computing device900may be a mobile device (e.g., a mobile telephone, a smartphone, a PDA, a tablet, a laptop, a camera, a tracker, a watch, a wearable device, etc.). In some embodiments, the computing device900may be a non-mobile device (e.g., a desktop computer or another type of client device). Further, the computing device900may be a server device that includes cloud-based processing and storage capabilities.

As shown inFIG.9, the computing device900can include one or more processor(s)902, memory904, a storage device906, input/output interfaces908(or “I/O interfaces908”), and a communication interface910, which may be communicatively coupled by way of a communication infrastructure (e.g., bus912). While the computing device900is shown inFIG.9, the components illustrated inFIG.9are not intended to be limiting. Additional or alternative components may be used in other embodiments. Furthermore, in certain embodiments, the computing device900includes fewer components than those shown inFIG.9. Components of the computing device900shown inFIG.9will now be described in additional detail.

In particular embodiments, the processor(s)902includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, the processor(s)902may retrieve (or fetch) the instructions from an internal register, an internal cache, memory904, or a storage device906and decode and execute them.

The computing device900includes memory904, which is coupled to the processor(s)902. The memory904may be used for storing data, metadata, and programs for execution by the processor(s). The memory904may include one or more of volatile and non-volatile memories, such as Random-Access Memory (“RAM”), Read-Only Memory (“ROM”), a solid-state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. The memory904may be internal or distributed memory.

The computing device900includes a storage device906includes storage for storing data or instructions. As an example, and not by way of limitation, the storage device906can include a non-transitory storage medium described above. The storage device906may include a hard disk drive (HDD), flash memory, a Universal Serial Bus (USB) drive or a combination these or other storage devices.

As shown, the computing device900includes one or more I/O interfaces908, which are provided to allow a user to provide input to (such as user strokes), receive output from, and otherwise transfer data to and from the computing device900. These I/O interfaces908may include a mouse, keypad or a keyboard, a touch screen, camera, optical scanner, network interface, modem, other known I/O devices or a combination of such I/O interfaces908. The touch screen may be activated with a stylus or a finger.

The computing device900can further include a communication interface910. The communication interface910can include hardware, software, or both. The communication interface910provides one or more interfaces for communication (such as, for example, packet-based communication) between the computing device and one or more other computing devices or one or more networks. As an example, and not by way of limitation, communication interface910may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. The computing device900can further include a bus912. The bus912can include hardware, software, or both that connects components of computing device900to each other.

FIG.10illustrates an example network environment1000of the event recurrence projection system106. The network environment1000includes a client device1006(e.g., client device108), an event recurrence projection system106, and a third-party system1008connected to each other by a network1004. AlthoughFIG.10illustrates a particular arrangement of the client device1006, the event recurrence projection system106, the third-party system1008, and the network1004, this disclosure contemplates any suitable arrangement of client device1006, the event recurrence projection system106, the third-party system1008, and the network1004. As an example, and not by way of limitation, two or more of client device1006, the event recurrence projection system106, and the third-party system1008communicate directly, bypassing network1004. As another example, two or more of client device1006, the event recurrence projection system106, and the third-party system1008may be physically or logically co-located with each other in whole or in part.

Moreover, althoughFIG.10illustrates a particular number of client devices1006, event recurrence projection systems104, third-party systems1008, and networks1004, this disclosure contemplates any suitable number of client devices1006, event recurrence projection system106, third-party systems1008, and networks1004. As an example, and not by way of limitation, network environment1000may include multiple client devices1006, event recurrence projection system106, third-party systems1008, and/or networks1004.

This disclosure contemplates any suitable network1004. As an example, and not by way of limitation, one or more portions of network1004may include an ad hoc network, an intranet, an extranet, a virtual private network (“VPN”), a local area network (“LAN”), a wireless LAN (“WLAN”), a wide area network (“WAN”), a wireless WAN (“WWAN”), a metropolitan area network (“MAN”), a portion of the Internet, a portion of the Public Switched Telephone Network (“PSTN”), a cellular telephone network, or a combination of two or more of these. Network1004may include one or more networks1004.

Links may connect client device1006, the event recurrence projection system106, and third-party system1008to network1004or to each other. This disclosure contemplates any suitable links. In particular embodiments, one or more links include one or more wireline (such as for example Digital Subscriber Line (“DSL”) or Data Over Cable Service Interface Specification (“DOCSIS”), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (“WiMAX”), or optical (such as for example Synchronous Optical Network (“SONET”) or Synchronous Digital Hierarchy (“SDH”) links. In particular embodiments, one or more links each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link, or a combination of two or more such links. Links need not necessarily be the same throughout network environment1000. One or more first links may differ in one or more respects from one or more second links.

In particular embodiments, the client device1006may be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by client device1006. As an example, and not by way of limitation, a client device1006may include any of the computing devices discussed above in relation toFIG.9. A client device1006may enable a network user at the client device1006to access network1004. A client device1006may enable its user to communicate with other users at other client devices1006.

In particular embodiments, the client device1006may include a requester application or a web browser, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLA FIREFOX, and may have one or more add-ons, plug-ins, or other extensions, such as TOOLBAR or YAHOO TOOLBAR. A user at the client device1006may enter a Uniform Resource Locator (“URL”) or other address directing the web browser to a particular server (such as server), and the web browser may generate a Hyper Text Transfer Protocol (“HTTP”) request and communicate the HTTP request to server. The server may accept the HTTP request and communicate to the client device1006one or more Hyper Text Markup Language (“HTML”) files responsive to the HTTP request. The client device1006may render a webpage based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable webpage files. As an example, and not by way of limitation, webpages may render from HTML files, Extensible Hyper Text Markup Language (“XHTML”) files, or Extensible Markup Language (“XML”) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a webpage encompasses one or more corresponding webpage files (which a browser may use to render the webpage) and vice versa, where appropriate.

In particular embodiments, event recurrence projection system106may be a network-addressable computing system that can interface between two or more computing networks or servers associated with different entities such as financial institutions (e.g., banks, credit processing systems, ATM systems, or others). In particular, the event recurrence projection system106can send and receive network communications (e.g., via the network1004) to link the third-party system1008. For example, the event recurrence projection system106may receive authentication credentials from a user to link a third-party system1008such as an online banking system to link an online bank account, credit account, debit account, or other financial account to a user account within the event recurrence projection system106. The event recurrence projection system106can subsequently communicate with the third-party system1008to detect or identify balances, transactions, withdrawal, transfers, deposits, credits, debits, or other transaction types associated with the third-party system1008. The event recurrence projection system106can further provide the aforementioned or other financial information associated with the third-party system1008for display via the client device1006. In some cases, the event recurrence projection system106links more than one third-party system1008, receiving account information for accounts associated with each respective third-party system1008and performing operations or transactions between the different systems via authorized network connections.

In particular embodiments, the event recurrence projection system106may interface between an online banking system and a credit processing system via the network1004. For example, the event recurrence projection system106can provide access to a bank account of a third-party system1008and linked to a user account within the event recurrence projection system106. Indeed, the event recurrence projection system106can facilitate access to, and transactions to and from, the bank account of the third-party system1008via a client application of the event recurrence projection system106on the client device1006. The event recurrence projection system106can also communicate with a credit processing system, an ATM system, and/or other financial systems (e.g., via the network1004) to authorize and process credit charges to a credit account, perform ATM transactions, perform transfers (or other transactions) between user accounts or across accounts of different third-party systems1008, and to present corresponding information via the client device1006.

In particular embodiments, the event recurrence projection system106includes a model (e.g., a machine learning model) for approving or denying transactions. For example, the event recurrence projection system106includes a transaction approval machine learning model that is trained based on training data such as user account information (e.g., name, age, location, and/or income), account information (e.g., current balance, average balance, maximum balance, and/or minimum balance), credit usage, and/or other transaction history. Based on one or more of these data (from the event recurrence projection system106and/or one or more third-party systems1008), the event recurrence projection system106can utilize the transaction approval machine learning model to generate a prediction (e.g., a percentage likelihood) of approval or denial of a transaction (e.g., a withdrawal, a transfer, or a purchase) across one or more networked systems.

The event recurrence projection system106may be accessed by the other components of network environment1000either directly or via network1004. In particular embodiments, the event recurrence projection system106may include one or more servers. Each server may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server. In particular embodiments, the event recurrence projection system106may include one or more data stores. Data stores may be used to store various types of information. In particular embodiments, the information stored in data stores may be organized according to specific data structures. In particular embodiments, each data store may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client device1006, or an event recurrence projection system106to manage, retrieve, modify, add, or delete, the information stored in data store.

In particular embodiments, the event recurrence projection system106may provide users with the ability to take actions on various types of items or objects, supported by the event recurrence projection system106. As an example, and not by way of limitation, the items and objects may include financial institution networks for banking, credit processing, or other transactions, to which users of the event recurrence projection system106may belong, computer-based applications that a user may use, transactions, interactions that a user may perform, or other suitable items or objects. A user may interact with anything that is capable of being represented in the event recurrence projection system106or by an external system of a third-party system, which is separate from event recurrence projection system106and coupled to the event recurrence projection system106via a network1004.

In particular embodiments, the event recurrence projection system106may be capable of linking a variety of entities. As an example, and not by way of limitation, the event recurrence projection system106may enable users to interact with each other or other entities, or to allow users to interact with these entities through an application programming interfaces (“API”) or other communication channels.

In particular embodiments, the event recurrence projection system106may include a variety of servers, sub-systems, programs, modules, logs, and data stores. In particular embodiments, the event recurrence projection system106may include one or more of the following: a web server, action logger, API-request server, transaction engine, cross-institution network interface manager, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, user-interface module, user-profile (e.g., provider profile or requester profile) store, connection store, third-party content store, or location store. The event recurrence projection system106may also include suitable components such as network interfaces, security mechanisms, load balancers, failover servers, management-and-network-operations consoles, other suitable components, or any suitable combination thereof. In particular embodiments, the event recurrence projection system106may include one or more user-profile stores for storing user profiles and/or account information for credit accounts, secured accounts, secondary accounts, and other affiliated financial networking system accounts. A user profile may include, for example, biographic information, demographic information, financial information, behavioral information, social information, or other types of descriptive information, such as interests, affinities, or location.

The web server may include a mail server or other messaging functionality for receiving and routing messages between the event recurrence projection system106and one or more client devices1006. An action logger may be used to receive communications from a web server about a user's actions on or off the event recurrence projection system106. In conjunction with the action log, a third party-content-object log may be maintained of user exposures to third party-content objects. A notification controller may provide information regarding content objects to a client device1006. Information may be pushed to a client device1006as notifications, or information may be pulled from client device1006responsive to a request received from client device1006. Authorization servers may be used to enforce one or more privacy settings of the users of the event recurrence projection system106. A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by the event recurrence projection system106or shared with other systems, such as, for example, by setting appropriate privacy settings. Third party-content-object stores may be used to store content objects received from third parties. Location stores may be used for storing location information received from client devices1006associated with users.

In addition, the third-party system1008can include one or more computing devices, servers, or sub-networks associated with internet banks, central banks, commercial banks, retail banks, credit processors, credit issuers, ATM systems, credit unions, loan associates, brokerage firms, linked to the event recurrence projection system106via the network1004. A third-party system1008can communicate with the event recurrence projection system106to provide financial information pertaining to balances, transactions, and other information, whereupon the event recurrence projection system106can provide corresponding information for display via the client device1006. In particular embodiments, a third-party system1008communicates with the event recurrence projection system106to update account balances, transaction histories, credit usage, and other internal information of the event recurrence projection system106and/or the third-party system1008based on user interaction with the event recurrence projection system106(e.g., via the client device1006). Indeed, the event recurrence projection system106can synchronize information across one or more third-party systems1008to reflect accurate account information (e.g., balances, transactions, etc.) across one or more networked systems, including instances where a transaction (e.g., a transfer) from one third-party system1008affects another third-party system1008.