Machine learning-based methods and systems for modeling user-specific, activity specific engagement predicting scores

A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data. A computing device is instructed to present at least one user-specific activity-related action instruction.

FIELD OF TECHNOLOGY

The present disclosure generally relates to improved machine learning-based systems, and more specifically to machine learning based methods and systems for modeling user-specific, activity-specific engagement predicting scores.

BACKGROUND OF TECHNOLOGY

A computer network system may include a group of computers (e.g., clients, servers, smart routers) and other computing hardware devices that are linked together through one or more communication channels to facilitate communication and/or resource-sharing, via one or more specifically programmed graphical user interfaces (GUIs) of the present disclosure, among a wide range of users.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplary technically improved computer-based method that includes receiving, by a machine-learning processor, an instruction to model at least one user-specific activity-specific engagement predicting score for at least one user from a plurality of users. User-specific, activity-specific data may be obtained, by the machine-learning processor, from a plurality of digital resources, based on the instruction, where the user-specific, activity-specific data may include: (i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user, (ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and (iii) at least one user-specific personal data of the at least one user. A user-specific activity engagement labeling data for the at least one activity may be predicted, by the machine-learning processor, by utilizing a first-type data pipeline on the at least one user-specific activity performance data. A plurality of user-specific, activity-specific data features may be predicted, by the machine-learning processor, by utilizing a second-type data pipeline on the user-specific, activity-specific data. The at least one user-specific activity-specific engagement predicting score may be predicted, by the machine-learning processor, based on at least one machine-learning model, by utilizing: (i) the user-specific activity engagement labeling data for the at least one activity, and (ii) the plurality of user-specific, activity-specific data features. At least one computing device may be instructed, by the machine-learning processor, based on the at least one user-specific activity-specific engagement predicting score, to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.

In some embodiments, the present disclosure provides an exemplary technically improved computer-based system that includes at least the following components a memory and a machine-learning processor which executes computer code that causes the machine-learning processor to receive an instruction to model at least one user-specific activity-specific engagement predicting score for at least one user from a plurality of users, to obtain from a plurality of digital resources, based on the instruction, user-specific, activity-specific data, where the user-specific, activity-specific data may include (i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user, (ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and (iii) at least one user-specific personal data of the at least one user, to predict a user-specific activity engagement labeling data for the at least one activity by utilizing a first-type data pipeline on the at least one user-specific activity performance data, to predict a plurality of user-specific, activity-specific data features by utilizing a second-type data pipeline on the user-specific, activity-specific data, to predict based on at least one machine-learning model, the at least one user-specific activity-specific engagement predicting score, by utilizing (ii) the user-specific activity engagement labeling data for the at least one activity, and (ii) the plurality of user-specific, activity-specific data features, and to instruct based on the at least one user-specific activity-specific engagement predicting score, at least one computing device to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.

DETAILED DESCRIPTION

Embodiments of the present disclosure herein disclose systems and methods for increasing yields from securities-based lending (SBL) products issued by an entity. At least one machine learning model (MLM) may be trained to predict via a utilization prediction score for each of a plurality of potential borrowers having a higher likelihood of generating high yields through the analysis of previous borrowing patterns. The at least one MLM may be configured to recommend actions to be taken by investment advisors to maximize yields from the portfolios of the potential borrowers targeted from a plurality of customers. The at least one MLM may be configured to suggest drawdown and/or payback actions aimed at current borrowers to minimize tax expenditure, banking fees and other taxes/commissions. Stated differently, the at least one MLM is configured to predict, for a given loan/line of credit (e.g., SBL), the probability that the line of credit will be used by a borrower in a predefined time interval after the line of credit is opened, such as a year, for example.

A plurality of user-specific data objects may be associated with a plurality of users and managed by a server associated with an entity. The plurality of user-specific data objects may include a plurality of user-specific activity performance data (e.g., debts/loans/lines of credit), a plurality of object data (e.g., assets/financial accounts) that allows the user to perform the at least one activity (e.g., since the asset and financial accounts provide collateral for the loan and/or line of credit), and/or personal data (e.g., PII, age, gender, demographic attributes, psychographic attributes, and/or behavioral attributes).

Optionally and/or alternatively, data may be stored may be stored in data records. Thus, the plurality of user-specific data objects may store obligation-based data records (e.g., debts, loans, and/or lines of credit), asset-based data records (e.g., assets/financial accounts), and/or personal data records, for example as shown herein below inFIG.1.

The entity server may transfer to a customer's account, an SBL line of credit to the customer's debt portfolio such that if used by the customer, the line of credit may generate yields and value for the entity. The entity may maintain a database of entity data records of the lines of credit given to any of the plurality of customers. The likelihood that a customer may use a line of credit based on the customer's asset portfolio, and debt portfolio as well as historical activity data in the debt portfolio may be modeled as a utilization prediction score outputted by a machine learning model. The utilization prediction score may also be referred to herein as a user-specific activity specific engagement predicting score.

In some embodiments, the utilization prediction score may be indicative of a first likelihood that the at least one user will use a line of credit generating revenue for the entity. In other embodiments, the utilization prediction score may be indicative of a second likelihood that the at least one user will churn, or close, the line of credit after given to the at least one user by the entity, where churning reduces revenue for the entity. In yet other embodiments, the utilization prediction score may be based on both the first and second likelihoods.

In some embodiments, a user obtaining a line of credit from an entity such as a financial institution. The term churning as used herein may refer to the user closing the line of credit without having drawn-down on the line of credit. In other embodiments, the term churning as used herein may refer to the user closing the line of credit having drawn-down on the line of credit. As churn reduces income yield to the lending entity, the entity may provide incentive(s) in order to reduce churn such as, for example, lower fees, lower interest rates or other incentives to utilize the line of credit. In some embodiments features which may impact the probability of churn include tenure (length of time since the line of credit was established), market value trends (of the underlying assets under management), cash trends (within the account) and the number of days since the most recent activity on the line of credit account.

FIG.1is a block diagram of a system10for modeling user-specific, activity-specific engagement predicting scores in accordance with one or more embodiments of the present disclosure. System10may include a server15, P computing devices90A and90B where P is an integer, and/or M electronic resources100A and100B denoted ELECTRONIC RESOURCE1. . . ELECTRONIC RESOURCEM all communicating35over a communication network.

In some embodiments, the server15may be associated by an entity or a financial entity that may provide securities-based lending (SBL) lines of credit to users (e.g., customers) such as a user80A and a user80B by an entity user (e.g., a financial advisor) such as an entity user85A and an entity user85B. The entity user (e.g., the banker or financial advisor) may perform the profiling analyses for at least one user using the system10to determine a likelihood that the at least one user may use the SBL line of credit offered by the financial institution and thus, generate yields for the financial institution when the at least one user uses the SBL line of credit. The entity user, such as a banker, financial advisor and the like, may use the user-specific output data from system10to determine whether or not to offer the SBL line of credit to the at least one user (e.g., the at least one customer).

In some embodiments, an electronic resource or digital resource in the context used herein may refer to, but not limited to a resource in which a user's financial and/or personal data may be stored in a plurality of data elements such as in a storage device of any bank and/or financial entity computing server. An electronic resource may also include, for example, social media and/or other data repositories such as Facebook, Twitter, Google, Instagram, and/or LinkedIn, for example, and accessible over the communication network30with user-specific data that may be useful in determining both creditworthiness and/or SBL credit line usage. The terms electronic resource and digital resource may be used interchangeably herein.

In some embodiments, the server15may include a machine-learning processor20for executing, in part, machine-learning and/or prediction algorithms, input and/or output (I/O) devices25, a communication circuitry40and a memory45. The machine-learning processor20may execute software code in software modules for performing the functions described herein. The software modules may include a data aggregator21, a time-series extraction pipeline22, a features extraction pipeline23, a machine learning model24, a prediction outcome manager26, and/or a graphic user interface (GUI) Manager27.

In some embodiments, the memory45may store an entity server (ES) database50and/or a user-specific data object database60. The ES database50may include a plurality of Q ES data records where Q is an integer denoted by ES DATA RECORD151. . . ES DATA RECORDQ52.

In some embodiments, the user-specific data object database60may include a plurality of N data objects where N is an integer denoted by DATA OBJECT1/USER162. . . DATA OBJECTN/USERN70. Each data object in the user-specific data object database60may be used for holding data records related to a unique user. For example, the DATA OBJECT1/USER162may include at least one obligation-based data record64, at least one asset-based data record65, and/or at least one personal data record67. The term portfolio65may be used synonymously with the at least one asset-based data record65that may include all of USER1's assets, bank accounts, securities holding, etc. Similarly, the DATA OBJECTN/USERN70may include at least one obligation-based data record71, at least one asset-based data record75, and/or at least one personal data record73. A term portfolio75of USERN may be used synonymously with the at least one asset-based data record72that may include all of USERN's assets, bank accounts, securities holding, etc.

In some embodiments, the user80A may interact with the entity user85A. The entity user85A may enter personal details and/or financial details of the user80A into a graphic user interface192A denoted GUI1of the computing device90A that is in communication35to transmit to and/or receive data from the server15. Similarly, the user80B may interact with the entity user85B. The entity user85B may enter personal details and/or financial details of the user80B into a graphic user interface192B denoted GUIP of the computing device90B that is in communication35to transmit to and/or receive data from the server15.

In some embodiments, the computing device90A may include a processor191A, a memory93A, a communication circuitry94A for communicating35over the communication network30, and input and/or output (I/O) devices95A. The processor191A may receive instructions from GUI Manager27to control the GUI192A via the communication network30. Similarly, the computing device90B may include a processorP91B, a memory93B, a communication circuitry94B for communicating35over the communication network30, and input and/or output (I/O) devices95B. The processorP91B may receive instructions from GUI Manager27to control the GUIP92B via the communication network30.

FIG.2is a flow diagram110for modeling user-specific, activity-specific engagement predicting scores in accordance with one or more embodiments of the present disclosure. The flow diagram110may represent a top-level flow diagram of the method for modeling user-specific, activity-specific engagement predicting scores that may include a gathering step S1115, a creation and/or update step S2120of a borrower's persona, a recommendation step S3125for recommending the next-best action for getting the user to apply for an SBL line of credit and to use it, and a monitoring step S4130where yields generated from the user's use of the SBL line of credit may be monitored. The utilization prediction score may be a loan-to-value (LTV) metric assigned to the user.

In some embodiments, in the gathering step S1115, a series of data transforms, aggregations, and filtering (i.e. an algorithm) may be used to create a proprietary set of information that may include data queried and structured in a specific way in order to provide information to a user or a system; also known as a “data feature” stored in data records in the electronic resources. This data may include (i) historical user-specific data that may be collected from any of the plurality of electronic resources100A and100B associated with financial institutions, (ii) data shared by the users (e.g. borrowers) about themselves, their assets, and any other user-specific personal data, (iii) social media and other digital data related to the borrower's digital footprint from any of the plurality of electronic resources100A and100B associated with social media sites and/or databases, and/or (iv) third-party data from any of the plurality of electronic resources100A and100B associated with third-party data websites and/or databases.

In some embodiments, the data aggregator21may receive historical data over the communication network30related to the borrower such as historical borrowing data may be used to generate data features that may include, but are not limited to, the composition of each portfolio (and the ratios of each asset in relation to every other asset), changes in composition, transactions, currently pledged and non-pledged collateral, abandoned loan applications, drawdown and payback rates, market movement in underlying pledged and non-pledged positions for customers, duration, amount, credit worthiness as well as ratios, trends, averages, medians, correlations data element stored in the obligation-based data record of a particular user (e.g., borrower).

In some embodiments, the data aggregator21may receive data shared by borrowers may include personal identification information (PII) that the borrower may share with a financial institution to get access to services and products. Typical examples of such information are (i) Name and Surname, (ii) Full Address, (iii) Social Security number, and/or (iv) date of birth.

In some embodiments, the data aggregator21may receive data from social media and other data repositories. This may be data that the borrower has already shared on social media and on other digital repositories. Examples of such repositories may include Linkedin, Twitter, Facebook, Instagram as well as Demographic and psychographic consumer databases (e.g., Acxiom).

In some embodiments, the data aggregator21may receive third party data that may include a broad range of institutional grade data, including real-time and historical stock prices, fundamentals, forex, and/or cryptocurrency. Moreover, a broad range of financial news may be collected and aggregated by third-party providers that may be a very valuable source of information to detect macro events that may influence borrower's behaviors and driving yields.

In some embodiments, given a borrower's PII data point, all of the data sources stored in the plurality of electronic resources as described above may be scanned to associate data relevant to the target borrower. In particular, pattern matching, Natural Language processing (NLP), probabilistic analyses, or any combination thereof may be used to associate the information found in the plurality of electronic resources to the PII data of the borrower.

FIG.3schematically illustrates a set of data attributes140of a user145from aggregated user-specific data in accordance with one or more embodiments of the present disclosure. The aggregated user-specific data may be used to create and/or update the Borrower's persona as in step S2120ofFIG.2. The set of attributes140for the user145named Richard Bryce, for example, may be defined at three levels: a product layer150, a sales layer160, and a borrower layer170.

In some embodiments, the data attributes associated with the borrower layer170may include data features that may be identified where customers who have pledged marketable securities against a non-purpose loan which may be labelled according to specific pre-defined customer segments. The data attributes may include PII Data162, a borrower profile164, an investment profile166, a portfolio composition168, and a market influence172. This analysis may be used to seed machine learning algorithms which may provide the basis to identify likely borrower behavior across the broader SBL loan book that may also be applied to the company's wider wealth client base to identify prospective borrowers and their likely borrower behavior. In addition, the investment profile and portfolio composition may by captured in the borrower layer170.

In some embodiments, the data attributes associated with the sales layer160may include attributes that are instrumental to sales and marketing operations. The data attributes may include a sales goal146, a lifetime value148, wealth152, and a churn likelihood154. In particular, attributes such as the lifetime value148and the churn likelihood154may be used by salesforce-tailor marketing messages and may be used to prioritize communication with certain cohorts of borrowers.

In some embodiments, the data attributes of the product layer150may include attributes such as utilization drivers142and a product recommendation144that capture the interaction of the borrower with products or the suitability for a specific product. These attributes may be evaluated simultaneously by running machine learning algorithms. The training and test data may be used to generate machine learning models for each attribute described above.

In some embodiments, the machine learning model24may be validated against labelled customer data in order to determine the optimal algorithm for a given set of customers. As new customers with marketable securities collateral are provided, the machine learning model24may be re-trained to provide an optimized algorithm by dynamically adjusting the parameters of the features and the underlying code that generates the features each runtime.

In some embodiments, the user-specific data attributes from step S2120ofFIG.2may be inputted to the machine learning model24. The machine learning model24may output in the step S3125on the GUI192A . . . GUIP92B, the recommended next-best-action that the entity user or the financial advisor may take to increase the likelihood that the user may use an SBL line of credit if offered. For example, once the borrower's attributes are calculated, they can be used to suggest next actions. Such actions may be taken programmatically such as by the server15automatically sending email messaging about the SBL line of credit, for example, or by a sales representative calling a borrower likely to churn upon reading the outputted recommended next-best-action for the borrower on the GUI192A . . . GUIP92B. When applied to multiple customers, of particular interest may be the ability to create multi-level, hyper-customized marketing campaigns targeting cohorts of borrowers that are more likely to generate yields for the financial institution.

In some embodiments, the terms “features” and “attributes” may be used interchangeably herein.

In some embodiments, if the SBL line of credit may be underutilized, a prescriptive action may be to implement a marketing campaign to generate movement and to prevent SBL churning. If the SBL line of credit may be overutilized, a prescriptive action may be to alert the borrower alternative ways to get credit. If the SBL line of credit may be leveraged, a prescriptive action may be to alert the borrower. If the SBL line of credit may be non-optimized with different line movement patterns, such as with Big-Small movements, for example, a prescriptive action may be to alert the borrower to suggest alternative ways to get credit. The prescription actions may also be referred to as user-specific, activity-related actions.

In some embodiments, the prediction outcome manager26may be used in Step S4130to monitor yields and to evaluate the impact of each prescriptive action on borrower behavior that may be put in place based on the machine learning models24. Based on the monitoring results, the prescriptive actions on borrower behavior may be optimized.

In some embodiments, the machine learning model24may perform the functions of the prediction outcome manager26.

FIG.4is a flow diagram of a time-series extraction process data pipeline200in accordance with one or more embodiments of the present disclosure. The time-series extraction process data pipeline200(e.g., the time-series extraction pipeline22ofFIG.1) may be configured to extract outstanding loan balances for the plurality of users that may be implemented in three steps: a data harvest step205, a data analysis step210, and a data aggregation step215. The time-series extraction process data pipeline200may be used to generate outstanding balance time series and to collect user-specific information from the loans data pulled directly via an application programmable interface (API), for example. The time-series extraction pipeline may also be referred to herein as a first-type data pipeline.

In some embodiments, the data harvest step205may include the machine-learning processor20pulling data from the plurality of electronic resources100A and100B in a [T01]APIs data harvest step220via at least one API. The loan data230may be stored locally in the obligation-based data records64of the user-specific data object database60in a [T02] local data cache step225. The loan data230may include an obligator, an obligation, a credit policy ID, a Commitment Amount Outstanding Balance that may further include a primaryBorrowerID, an internalContactID, an evaluationID, a CollateralAccountID, and a lineOfBusiness ID.

In some embodiments, the data analysis step210may include the machine-learning processor20assessing the new data quality in a [T03] Data Quality assessment step240by both manual and/or automated processes. In some embodiments, the machine-learning processor20may execute a [T04] New Loan Detection step235to detect new loans with respect to the last time that data was pulled in step235.

In some embodiments, the data aggregation step215may include an outstanding balance time series (TS) creation [T05] step245in order to extract the history of each loan from our local data from the local data cache225. In order to compute the outstanding value of a given loan for all days in a [T06] Timeseries filling step250, the machine-learning processor20may fill missing values into many outstanding balance time series outputted from the time series (TS) creation [T05] step245. The machine-learning processor20may interpolate data within missing data time intervals in the Timeseries filling step250. This process may lead to two results for each loan: [O1] outstanding balances time series (TS) dataset for each loan [O1]255and [O2] a Loans Summary260, or a summarized information about the loan itself [O2], which may include for each loan, the committed amount [O2] and other descriptive information such as, for example, a start and an end date for each loan.

In some embodiment, the system10may be configured to classify borrowers through the distribution of their assets. To represent a distribution, the machine-learning processor20may use a frequency count feature that may capture the portion of wealth allocated to a particular asset or category of asset. The machine-learning processor20may classify the portfolio composition by first considering two different aspects: (i) market value, and (ii) haircuts applied by financial institutions while offering a loan.

FIG.5is a flow diagram of a feature extraction data pipeline300in accordance with one or more embodiments of the present disclosure. The feature extraction data pipeline300(e.g., the feature extraction pipeline23ofFIG.1) may include a setup phase305, a classification phase310, and a generation phase315. The feature extraction data pipeline300may be used to generate portfolio features for training the machine learning model24using historical financial data of borrowers. The feature extraction pipeline may also be referred to herein as a second-type data pipeline.

In some embodiments, the machine-learning processor20may execute the feature extraction pipeline23(FIG.1) or feature extraction data pipeline300ofFIG.5. In the setup phase305, the machine-learning processor20may use as an input the dataset of [O1] outstanding balances time series dataset320, a portfolio date/features type325, and/or the historical financial data from the local data cache [T02]335into a [P01] Assets Vocabulary Creation module330. In other embodiments, the machine-learning processor20may filter out from the borrower's financial data, all loans having less than 365 days of historical data. The [P01] Assets Vocabulary Creation module330may create a dictionary of all possible asset IDs within the financial data.

In some embodiments, the machine-learning processor20, in the classification phase310, may input an asset class name [S01] lookup table345and a Committee on Uniform Security Identification Procedures (CUSIP) code [S02] lookup table355into an Asset Hierarchical classification [P02] module310. These lookup tables respectively describe the class and/or the sector that a given asset may belong to with three levels of granularity. This information may be attached to all loan data in the user-specific object database60so that each asset may be described in a very detailed fashion [P02].

In some embodiments, the machine-learning processor20, in the generation phase315, using an aggregation module360may aggregate assets by feature_type [P03] so as to split each portfolio (in terms of either market value or top up amount) according to different types of segmentation. The machine-learning processor20may consider the amount of each asset of the vocabulary generated by module330(i.e., [P01]), For each class in asset class name [S01] lookup table345, the machine-learning processor20may use the total amount of assets belonging to that class. Each class then has two more granularity levels. For each sector in (CUSIP) code [S02] lookup table355, the machine-learning processor20may use the total amount of assets belonging to that sector. Each sector then has two more granularity levels.

In some embodiments, for each portfolio and for each segmentation, the machine-learning processor20may generate a histogram representing how the portfolio may be composed in terms of the different categories of that specific segmentation type. In a normalization step365by feature_type [P04], each histogram may be normalized independently using L2-normalization. Each normalized histogram may represent a set of features. In a [P05] concatenation step370, the set of features may be fused together or concatenated so as to generate a common features vector. In a [P06] final normalization step375, each feature vector may be normalized independently through L2-normalization. The final result of the feature extraction data pipeline300is to generate a portfolio features dataset [O3] that may include the features of each portfolio that may be used to train the model.

FIG.6is an exemplary histogram400illustrating a total amount of portfolio assets420split into asset classes410versus each asset class430in accordance with one or more embodiments of the present disclosure.

FIG.7is an exemplary histogram500illustrating a total amount of portfolio assets520split into sectors520versus each sector530in accordance with one or more embodiments of the present disclosure.

In some embodiments, there may be a correlation between the probability of moving the credit line and the portfolio composition of the borrower that is opening a new loan which may be incorporated into the machine learning model24. Thus, the machine learning model24may be trained by using as training data, the portfolio corresponding to each loan. Since these parameters may change as a function of time, the machine-learning processor20may assess this correlation using the machine learning model24at the time at which the new credit line is opened.

FIG.8is a flow diagram600for classifying assets based on a number of attributes in accordance with one or more embodiments of the present disclosure. The machine-learning processor20may receive collateral holdings615of a user portfolio65and may [O1] sanitize the collateral holding in a step605to retrieve Assets_ID610, or asset-based identifiers. The machine-learning processor20may further extract an L0 Asset Id in a step620and an L0 classification in a step630.

In some embodiments, the machine-learning processor20may use an [S01] asset className lookup table and the data from the steps620and630to get the asset classification in a step640of the user portfolio65. The assets in the user portfolio65may be classified into an L1 asset class624, an L2 asset class644and/or an L3 asset class646. The L1 class624may include the asset main categories such as cash, equities, and funds, etc. The L2 class644may include asset subcategories such as Funds-ETFs, Fund-Bonds, Mutual-Funds, Cash, etc. The L3 class646may include Asset Sub-subcategories such as Cash-Calue Life Insurance, Cash-cash, Equity-Common shares, Equity-Convertible shares, Fund-US Equity Small Blend ETFs, etc.

In some embodiments, the machine-learning processor20may use an [S02] CUSIP lookup table645and the data from the steps620and630to get the asset sectors in a step650of the user portfolio65. The assets in the user portfolio65may be classified into an L4 Sector652, an L5 Sub-sector654and/or an L5 country656. The L4 Sector652may include the asset main sectors such as Industrials, Consumer Staples, Utilities, etc. The L5 Sub-sector654may include asset subsectors such as the Aerospace industry, Airline, Agriculture, etc. The L5 country656may include USA, Brazil, Germany, etc.

FIG.9is a flow diagram700of a label creation data pipeline in accordance with one or more embodiments of the present disclosure. The [L00] label creation data pipeline may be used to generate utilization labels for use in training the machine learning model24. In a first step in the label creation data pipeline may include detecting movements in the user portfolio65. The machine-learning processor20may [L01] detect a number of movements such as drawdowns/paybacks in a step710from the user portfolio65for each outstanding balances time-series (TS) dataset715over a time interval705defined by Labels_start_date to Labels_end_date. The machine-learning processor20may detect the movements by computing a first order derivative of a given times-series. The machine-learning processor20may count the number of movements that may have occurred in the first 365 days of each time series [L02], for example.

In some embodiments, in a second step in the label creation data pipeline may include labels creation. The machine-learning processor20may [L03] generate a utilization label for each loan in the user portfolio65in a step725according to the number of movements occurring in the outstanding balance within the first 365 days, for example. If this number is greater than 0, then the label is 1, otherwise the label is 0. In a step730, a [O4] Utilization Label is generated for each loan in the user portfolio65which may be added to the portfolio features vector for training the machine learning model24.

In some embodiments, the time-series extraction pipeline22or a first-type data pipeline may include a label creation functionality as described inFIG.9to generate labeled loan data also referred to as user-specific activity engagement labeling data.

FIG.10is a flow diagram800of a training data pipeline in accordance with one or more embodiments of the present disclosure. The training data pipeline may be used to create a model that is able to predict the utilization likelihood of a loan. In a step805denoted [51] Pull Data, the machine-learning processor20may update the content of historical financial data for the plurality of users by locally pulling data via the Fastnet API. The machine-learning processor20may then execute two parallel processes: a label creation process and a features extraction process.

In some embodiments, the machine-learning processor20may build a [S2] time series of the outstanding balance of each loan [S2] in a step810. According to the pattern of each time series, the machine-learning processor20may create a label [S3] in a step820. In parallel in a step830, the machine-learning processor20may create features from financial data of each portfolio [S4]. Once both features and labels for each loan have been generated, the machine-learning processor20may start training [S5] in a step825the machine learning model24to learn the relationship between the features and labels.

FIG.11is a flow diagram900of an incremental learning inference data pipeline in accordance with one or more embodiments of the present disclosure. The incremental learning inference data pipeline (e.g., a third type of data pipeline) describes how the machine learning model24learns from the training dataset. Suppose that there is a [M02] trained machine learning model515deployed in a production environment. Periodically. the incremental learning inference data pipeline may receive [O3] new portfolio features905and may predict [IL01] the utilization likelihood for each of them as utilization scores920.

In some embodiments, a user may perform a [IL02] manual review925of the processed portfolio to know if any of the predicted utilization labels were correct or not. If not correct, the predicted utilization labels that are incorrect may be adjusted in a step930and the model retrained [IL03] in a step935. In this manner, new portfolio feature data [O3-O4] and local annotated data in step940may be used to retrain a better ML model [IL03] in the step935. At this point the new model may be deployed and the process restarts, but with an improved utilization prediction model [M03] in a step950.

In some embodiments, a Light Gradient Boosting (LightGBM) model may be used since it provides an interpretation of the output. In particular, once the model is trained, the model provides an understanding as to both which features are the most discriminative ones for the whole training dataset and which features are the most important ones to determine the output of each test sample at inference time.

In some embodiments, the LightGBM framework may support different machine learning algorithms including gradient boosted trees (GBT), gradient boosted decision trees (GBDT), gradient boosted regression trees (GBRT), gradient boosted machine (GBM), multiple additive regression trees (MART) and random forest (RF). LightGBM may use the advantages of Extreme Gradient Boosting (XGBoost), including sparse optimization, parallel training, multiple loss functions, regularization, bagging, and early stopping. A major difference between the LightGBM and XGBoost may be in the construction of trees. LightGBM does not provide a tree level-wise—row by row—as most other implementations do, but grows trees leaf-wise. It may choose the leaf yielding the largest decrease in loss. LightGBM may not use a sorted-based decision tree learning algorithm, which may search for the best split point on sorted feature values, as XGBoost or other implementations do. Instead, LightGBM may implement a highly optimized histogram-based decision tree learning algorithm, which may yield advantages on both in terms of computational efficiency and memory consumption. The LightGBM algorithm may utilize two novel techniques called Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) which may allow the algorithm to run faster while maintaining a high level of accuracy. LightGBM may operate on Linux, Windows, and macOS platforms and may support C++, Python, R, and C#.

FIG.12is a first graph1000illustrating computational results in accordance with one or more embodiments of the present disclosure. The first graph1000illustrates the results for a first exemplary use case of single lead scoring. The first graph1000shows the results considering all loans in the test dataset. This accounts for a scenario in which there is a single lead and the system predicts how likely1020the single lead is going to move1050the credit line in the first 365 days from its hypothetical opening, not move the credit line1040, or a weighted outcome1060.

The first graph1000shows that for a given input lead (e.g., prospective customer), the machine learning (ML) model24may predict the likelihood that the lead will move the credit line during the first year after its opening. The first graph1000shows the performance of the ML model24, in terms of precision, recall and f1 (as shown in a graph legend1010), based on test set that include a set of loans to be used for training the ML model24.

The first group may refer to the NOT Moving sample1040, the second group to the Moving sample1050, while the weighted group1060refers to the weighted average results between the two classes. For example, for the Moving class1060, the precision of the Moving class is about 63%, such that for every 100 leads that the ML model24predicted will move the line of credit during the first year, 63 leads actually did. The recall of the Moving class1060is about 63%, such that for every 100 leads that will actually move the line of credit during the first year, the ML model24successfully detects63of them. The f1 is a trade-off metric between precision and recall and that may be typically used to summarize the overall performance of the ML model24. Note that the Precision, Recall and f1 metrics are also shown for the NOT moving1040and Weighted Average1060classes.

FIG.13is a second graph1100illustrating computational results in accordance with one or more embodiments of the present disclosure. In this case, the results may consider all loans in the test dataset but ranked1120by the prediction confidence score1110. This is a representation of the scenario in which a financial advisor has a list of leads and has to decide which one that the financial advisor should start trying to convince to utilize the loan line of credit. The second graph1100shows predicted results1125and baseline results1130.

The second graph1100describes the performances of the ML model24from a different perspective, that is to simulate the job of a financial advisor in selecting leads. The financial advisor may have, for example, a list of 100 possible leads. Without the ML model24, the financial advisor may randomly choose the first possible lead to engage with. In some embodiments, using the ML model24, the prediction outcome manager26may display to the financial advisor on the graphic user interface GUIP92B, for example, a list of leads ranked by the probability of drawing down a loan.

The x-axis1120of the second graph1100may indicate the specific percentage of the training test set whose samples have been ranked by a descending probability of opening a credit line. The y-axis of the second graph1100may indicate the precision in percentage of the ML model24on that percentage of training test set. In order for the ML processor20to generate this chart, the training test set sample was ranked according to the predicted probability of moving the line of credit (in descending order). For example, when x is 11%, this refers to the first 11% of the test samples being considered. The corresponding precision of the ML model24may be computed, which is about 69% for a financial advisor using the ML model24, while the precision may be only about 61% when the financial advisor does not use the ML model24(e.g., works alone).

FIG.14is a third graph1200illustrating computational results in accordance with one or more embodiments of the present disclosure. The third graph1200illustrates a Total Portfolio Market Value1210versus a yield probability1220of generating revenue by users using a line of credit over the first 12 months of SBL life. A legend1230of the third graph1200shows data points for Ultra High Net Worth Individuals (UHNWI) T4, High Net Worth Individuals (HNWI) T3, a retail customer T2, and a retail customer T1. Each point on the third graph1200may be a sample lead in the test set. The x-axis1220may indicate the probability that a given lead may fall into that line, such that the lead may move the line of credit during its first 12 months so as to generate yield. The y-axis1210is the total market value of each lead. The Total market value may be split in 4 value-based categories1230, each of which represents a particular client segment such as ultra-high net worth individual (UHNWI), high net worth individual (HNWI), etc.

FIG.15is a graph1300illustrating a potential usage of a line of credit in accordance with one or more embodiments of the present disclosure. The graph1300plots a commitment amount1310, such as how much potential LOC usage that a user will use from a given line of credit LOC, as a function of portfolio movements1320of the user. The graph1300indicates a labeling of users, such as a user of high potential1325for using the LOC of $3 million or more, for example, even though the user exhibits no-draw-down activity of the user's portfolio, a user of high value1335for using the LOC of $3 million or more, for example, with the user exhibits high utilization and/or frequent draw-down activity of the user's portfolio, a user of table stakes1330for using the LOC of $3 million or less, for example, and where the user exhibits no-draw-down activity of the user's portfolio, and a user of lifestyle1330for using the LOC of $3 million or less, for example, and where the user exhibits high utilization and/or frequent draw-down activity of the user's portfolio. The machine learning model24is configured to capture these parametric trends. A financial advisor should target a user that exhibits high utilization and/or frequent draw-down activity of the user's portfolio and may be able to handle a larger LOC. Not shown in the graph1300is where the user is determined not eligible to be offered an SBL Loan.FIG.15refers to a line of credit of $3 million, which is by way of example and not by limitation of the embodiments disclosed herein. Any suitable value of a line of credit (LOC) may be given to the user based on the disclosed methods.

FIG.16is an exemplary output1500on graphical user interface92A or92B in accordance with one or more embodiments of the present disclosure. When the financial advisor85A or85B (e.g., the entity-user) on any of the P computing devices90A or90B via GUI192A or GUIP92B runs at least one user for the system10to determine whether or not to grant the at the least one user an SBL LOC, the financial advisor may receive the an exemplary output1500in GUI92A or92B as shown inFIG.16. The exemplary output1500may display the same set of data attributes140for the at least one user (in this exemplary case for Richard Bryce145) as shown inFIG.3as well as an assessment1510of an influence of market volatility and/or interest rates on the user's portfolio value.

In some embodiments, the financial advisor run the algorithms for a single user80A or80B such as during a face-to-face meeting, for example, as shown inFIG.1. In other embodiments, the financial advisor may run the algorithms on a plurality of users and receive a ranked list of the plurality of users with a prediction utilization score for assessing whether each of the plurality of users may use the SBL line of credit.

In some embodiments, the exemplary output1500may include a financial advisor marketing kit such system10may provide the financial advisor with a personalized script1520, a personalized e-mail1530and/or a personalized video1540with suggested user-customer communication for a specific user from the at least one user.

In some embodiments, the model (e.g., the machine learning model24) may be executed by the machine-learning processor20as either a micro service or an API. Regardless, the model will receive as an input, a composition of the portfolio65a potential user (e.g., customer), which will generate user-specific features and predict how likely an SBL credit line corresponding to the portfolio65will be moved or used in the first 365 days from its opening.

In some embodiments, the machine-learning processor20may receive a plurality of portfolios of a plurality of users (e.g., client data gathering), determine SBL qualification for each of the plurality of users respectively based on each of their portfolios, use the model (e.g., the machine learning model24) to determine projected utilization and LTV ranking, and the provide actionable rankings on the GUI92A and92B for the financial advisors.

In some embodiments, training the machine learning model24may include the machine-learning processor20generating a dataset of input and output vectors of data from the plurality of user that may include the model input data, data features, and output data as classified below in Tables I, II, and III. During training, the input and output vectors may be applied to the input and the output of the machine learning model24to train the machine learning model24.

In some embodiments, Table I is a list of exemplary model inputs as shown below.

In some embodiments, Table II is a list of exemplary data features as shown below.

TABLE IIExemplary Data FeaturesExemplary Data Features:Dateyear: Year.quarter: Quarter.month: Month.week_of_year: Week of year.Tenuredays_since_loan_started: Days since the loan started.months_since_loan_started: Months since the loan started.Outstanding balanceoutstanding_balance: Outstanding balance.days_with_positive_os_qty: Quantity of dayswith positive outstanding balanceduring the entire length of the loan.days_with_positive_os_ratio: Quantity of dayswith positive outstanding balanceduring the entire length of the loan over thequantity of days since the loan started.days_since_os_zero_qty: Quantity of consecutive dayswith the outstanding balance been zero.Days_since_os_positive_qty: Quantity of consecutivedays with the outstanding balance been positiveDrawdownsdrawdowns_qty: Quantity of drawdowns.drawdowns_amount_sum: Total amount of money drawdowned.days_until_first_drawdown: Quantity of days elapsedwhen the first drawdown occurred.first_drawdown_amount: Amount of money drawdownedin the first drawdown.first_drawdown_commitment ratio: Amount of moneydrawdowned in the firstdrawdown over the commitment amount available.days_since_last_drawdown: How many days haveelapsed since the last drawdown.Paymentspayments_qty: Quantity of payments.payments_amount_sum: Total amount of money paid.drawdowns_amount_paid_ratio: Total amount ofmoney drawdowned that was already paid.AlertsIs_on_top_up_alert: Boolean value that indicates if theloan has an open top up alert.Is_on_sell_out_alert: Boolean value that indicates if theloan has an open sell out alert.Is_on_margin_alert: Boolean value that indicates if theloan has an open margin alert.days_since_top_up_alert_opened_qty: Quantity ofdays elapsed with an open top up alert.days_since_sell_out_alert_opened_qty: Quantity of days elapsedwith an open sell out alert.top_up_alerts_opened_qty: Quantity of top up alerts openedduring the entire length of the loan.sell_out_alerts_opened_qty: Quantity of sell out alertsopened during the entire length of the loan.margin_alerts_opened_qty: Quantity of margin alertsopened during the entire length of the loan.top_up_alerts_days_qty: Quantity of days with anopen top up alert during the entire length of the loan.sell_out_alerts_days_qty: Quantity of days with an opensell out alert during the entire length of the loan.margin_alerts_days_qty: Quantity of days with anopen margin alert during the entire length of the loan.PortfolioPercentage equity—percentage of portfolio in equitiesPercentage fixed income—percentage of portfolio in fixed incomePercentage MF—percentage of portfolio in mutual fundsPercentage ETF—percentage of portfolio in etfspercentage alternatives—percentage of portfolio in alternativesMV change 3 months—change in portfolio ($ value)over the last 3 monthsMV change 1 months—change in portfolio ($ value)over the last 1 monthsMV change 6 month—change in portfolio ($ value)over the last 6 monthsTotal MV—$ portfolio is worthAdvisor code—code of the advisorAdvisor institution—institution of the advisorCustodian—custodian of the account

In some embodiments, Table III is a list of exemplary outputs as shown below.

TABLE IIIExemplary OutputsExemplary OutputsClient NumberAccount NumberPropensity to Utilize LoanPropensity to Initiate LoanPropensity to Churn in 3 monthsPropensity to Churn in 6 monthsPropensity to Churn in 9 monthsPropensity to Churn in 12 monthsPropensity to migrate segmentsModel factors expressed as data features and % importance for eachpropensity measure

FIG.17is a flow diagram1600of a pull single lead scoring in accordance with one or more embodiments of the present disclosure. In a first step1620, any of the P the computing device processors such as the processor1or the processorP91A or91B of a financial advisor via GUI192A or GUIP92B operating banking customer relationship management (CRM) software (e.g., user80A or80B) may request to the machine-learning processor20over the communication network30to receive from the prediction outcome manager26a utilization prediction1630for the single user. In step2via a microservice, the machine-learning processor20may receive user-specific financial data from databases and electronic resources which may be input to the machine learning model24to receive a utilization prediction1630of a SBL line of credit for the single user. The utilization prediction may be outputted to the GUI192A or GUIP92B for the financial advisor to view as in Step31640. In this scenario, each of the computing devices may be a separate bank and the user-specific financial data may be relayed to the server15over the communication network30for determination of the utilization prediction score. In other embodiments, the processes shown in the flow diagram1600may be performed on the server15with all of the user-specific financial data for the single user stored in the user specific data object database60.

FIG.18is a flow diagram1700for scoring a batch process of leads in accordance with one or more embodiments of the present disclosure. In some embodiments, a bank portfolios database1705(e.g., such as any of the N user portfolios65or75) may be stored in any of the P memories93A or93B on any of the P computing devices90A or90B. In other embodiments, the bank portfolios database1705may be stored in the memory45of the server15. In a first step1710, a plurality of portfolios of a respective plurality of users may be input in a batch process1745to the machine learning model in a second1720that outputs a utilization prediction score1725for each of the plurality of users. in a third step1730, the bank portfolios database1705may be updated with the utilization prediction score1725for each of the plurality of users.

In some embodiments, the utilization prediction score1725for each of the plurality of users may be ranked and a ranked list may be displayed to a financial advisor on any of the P graphical user interfaces (e.g., GUI192A, GUIP92B inFIG.1).

FIG.19is a is a flowchart of an exemplary method1735for modeling user-specific, activity-specific engagement predicting scores in accordance with one or more embodiments of the present disclosure. The method1735may be performed by the machine-learning processor20.

The method1735may include receiving1740an instruction to model at least one user-specific activity-specific engagement predicting score for at least one user from a plurality of users.

In some embodiments, the instruction, for example, may be an electronic request from a financial advisor using GUI192A or GUIP92B over the communication network30for the server15to model at least one user-specific activity-specific engagement predicting score. The term “at least one user-specific activity-specific engagement predicting score” is synonymous and equivalent to the prediction utilization score that is the likelihood that the at least one user will use a line of credit as described herein above. In other embodiments, the prediction utilization score may include the likelihood that the at least one user will churn the line of credit with a higher score indicating a higher probability of user churning, and a lower score indicate a lower probability of user churning. The computation and/or modeling of the at least one user-specific activity-specific engagement predicting score is the performed by the machine learning processor20by applying the time-series extraction pipeline22and features extraction pipeline23to the user-specific, activity specific data.

In some embodiments, a first algorithm may be used for computing a first prediction utilization score that is the likelihood that the at least one user will use a line of credit. Likewise, a second algorithm may be used for computing a second prediction utilization score that is the likelihood that the at least one user will churn the line of credit. The first and second algorithms may be separate, independent, and/or decoupled from one another.

The user-specific, activity specific data may be financial data (e.g., both assets and/or debts), and/or personal data (age, gender, demographic attributes, psychographic attributes, and/or behavioral attributes) obtained from any of the digital resources and/or provided directly from the at least one user. Note that the terms digital resources and electronic resources may be used interchangeably herein and examples are provided as described herein above.

The method1735may include obtaining1745from a plurality of digital resources, based on the instruction, user-specific, activity-specific data where the user-specific, activity-specific data includes (i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user, (ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and (iii) at least one user-specific personal data of the at least one user.

In some embodiments, at least one user-specific activity performance data regarding performance of at least one activity by the at least one user may be, for example, historical data regarding loans (types, balances) and/or lines of credit (types, balances) provided to the user. The performance of the at least one activity may refer to historical data regarding the number of movements, and/or churning of loans and/or lines of credit made by the user.

In some embodiments, at least one object data for at least one object that allows the at least one user to perform the at least one activity may refer to the current and/or historical data of accounts and/or securities (e.g., balances, and/or transactions), for example, held in the user portfolio75that allows the user (e.g., by providing collateral for the user) to perform the at least one activity regarding movements and/or churning of old and/or new lines of credit.

In some embodiments, the at least one object of the at least one user may be the data object70unique to a particular Nth user from a plurality of users that stores data, such as in data records, for example, such as user debts, loans, and/or line of credit stored in obligation-based data record, asset-based data records75(such as the Nth user portfolio75, and user-specific personal data (e.g., PII, age, gender, demographic attributes, psychographic attributes, and/or behavioral attributes).

The method1735may include predicting1750a user-specific activity engagement labeling data for the at least one activity by utilizing a first-type data pipeline on the at least one user-specific activity performance data.

In some embodiments, the user-specific activity engagement labeling data may the data from outstanding balances TS dataset255and the loans summary260outputted from time-series extraction process data pipeline200(e.g., the first-type data pipeline) which may be then labeled as described in the label creation flow diagram700. In other embodiments, the processes of the label creation flow diagram700may be integrated directly or may be a part of the first-type data pipeline.

The method1735may include predicting1755a plurality of user-specific, activity-specific data features by utilizing a second-type data pipeline on the user-specific, activity-specific data.

In some embodiments, the user-specific, activity-specific data may be inputting into the feature extraction data pipeline300(e.g., the second-type data pipeline) which may output the plurality of user-specific, activity-specific data features as shown for example in Table II.

The method1735may include predicting1760based on at least one machine-learning model, the at least one user-specific activity-specific engagement predicting score, by utilizing: (i) the user-specific activity engagement labeling data for the at least one activity, and (ii) the plurality of user-specific, activity-specific data features.

The method1735may include instructing1765based on the at least one user-specific activity-specific engagement predicting score, at least one computing device to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.

In some embodiments, the at least one machine-learning model may model and predict the at least one user-specific activity-specific engagement predicting score and/or the at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user (e.g., at least one recommendation to the financial advisor as to how to cause a particular user to agree to accept and/or use the line of credit to generate revenue for the entity.)

The embodiments disclosed herein improve the overall computational efficiency of the server15in contrast to a computing system that processes terabytes of raw user-specific activity specific data for each user from a plurality of users to determine at least one user-specific activity-related action instruction that predicts a user-specific activity-related action to be performed with each user. Such user-specific activity-related actions may include but are not limited to establishing a line of credit within a specific timeframe (e.g., the next 12 months), drawing down a certain amount of funds from that line of credit within a specific timeframe or paying back funds against that line of credit within a specific timeframe, according to a specific temporal pattern(s) or other data driven pattern(s).

The technical improvements result from the machine learning processor20using the user-specific activity specific data to generate smaller datasets of labeled data and/or data features, which is transformed using the machine learning models to the user-specific activity-specific engagement predicting score for each user. The machine learning processor20may use the user-specific activity-specific engagement predicting score to instruct any of the P computing devices90A and90B to display a user-specific activity-related action to be performed with each user.

Thus, the ordered combination of the data pipelines disclosed herein to generate the smaller user-specific datasets, may be transformed by the machine learning processor20to output user-specific activity-specific engagement predicting score used for displaying a user-specific activity-related action to be performed with each user. These embodiments provide a technical improvement by significantly improving the computing speed and computationally efficiency relative to a system that merely processes the raw user-specific activity specific data. Such technical improvements enrich existing customer data with a layer of behavioral customer data. Using such behavioral data, multiple signals can be predicted in order for example but not limited to maximizing business metrics such as customer lifetime value, net margin contribution by customer and other key metrics.

In some embodiments, exemplary inventive, specially programmed computing systems/platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols/modes such as, without limitation, IPX/SPX, X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes. In some embodiments, the NFC can represent a short-range wireless communications technology in which NFC-enabled devices are “swiped,” “bumped,” “tap” or otherwise moved in close proximity to communicate. In some embodiments, the NFC could include a set of short-range wireless technologies, typically requiring a distance of 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to 424 kbit/s. In some embodiments, the NFC can involve an initiator and a target; the initiator actively generates an RF field that can power a passive target. In some embodiments, this can enable NFC targets to take very simple form factors such as tags, stickers, key fobs, or cards that do not require batteries. In some embodiments, the NFC's peer-to-peer communication can be conducted when a plurality of NFC-enable devices (e.g., smartphones) within close proximity of each other.

In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, a stand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product.

In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on.

In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming and/or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.

As used herein, the terms “proximity detection,” “locating,” “location data,” “location information,” and “location tracking” refer to any form of location tracking technology or locating method that can be used to provide a location of, for example, a particular computing device/system/platform of the present disclosure and/or any associated computing devices, based at least in part on one or more of the following techniques/devices, without limitation: accelerometer(s), gyroscope(s), Global Positioning Systems (GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonable form of wireless and/or non-wireless communication; WiFi™ server location data; Bluetooth™ based location data; triangulation such as, but not limited to, network based triangulation, WiFi™ server information based triangulation, Bluetooth™ server information based triangulation; Cell Identification based triangulation, Enhanced Cell Identification based triangulation, Uplink-Time difference of arrival (U-TDOA) based triangulation, Time of arrival (TOA) based triangulation, Angle of arrival (AOA) based triangulation; techniques and systems using a geographic coordinate system such as, but not limited to, longitudinal and latitudinal based, geodesic height based, Cartesian coordinates based; Radio Frequency Identification such as, but not limited to, Long range RFID, Short range RFID; using any form of RFID tag such as, but not limited to active RFID tags, passive RFID tags, battery assisted passive RFID tags; or any other reasonable way to determine location. For ease, at times the above variations are not listed or are only partially listed; this is in no way meant to be a limitation.

In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be configured to securely store and/or transmit data by utilizing one or more of encryption techniques (e.g., private/public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RCS, CAST and Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).

As used herein, a “financial instrument” refers to an equity ownership, debt or credit, typically in relation to a corporate or governmental entity, where the financial instrument is typically traded via one or more financial trading venues. Some examples of “financial instruments” can include, but are not limited to, stocks, bonds, commodities, swaps, futures, and currency.

FIG.20depicts a block diagram of an exemplary computer-based system/platform1800in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the exemplary inventive computing devices and/or the exemplary inventive computing components of the exemplary computer-based system/platform1800may be configured to manage a large number of members and/or concurrent transactions, as detailed herein. In some embodiments, the exemplary computer-based system/platform1800may be based on a scalable computer and/or network architecture that incorporates varies strategies for assessing the data, caching, searching, and/or database connection pooling. An example of the scalable architecture is an architecture that is capable of operating multiple servers.

In some embodiments, referring toFIG.20, members1802-1804(e.g., clients) of the exemplary computer-based system/platform1800may include virtually any computing device capable of receiving and sending a message over a network (e.g., cloud network), such as network1805, to and from another computing device, such as servers1806and1807, each other, and the like. In some embodiments, the member devices1802-1804may be personal computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, and the like. In some embodiments, one or more member devices within member devices1802-1804may include computing devices that typically connect using a wireless communications medium such as cell phones, smart phones, pagers, walkie talkies, radio frequency (RF) devices, infrared (IR) devices, CBs, integrated devices combining one or more of the preceding devices, or virtually any mobile computing device, and the like. In some embodiments, one or more member devices within member devices1802-1804may be devices that are capable of connecting using a wired or wireless communication medium such as a PDA, POCKET PC, wearable computer, a laptop, tablet, desktop computer, a netbook, a video game device, a pager, a smart phone, an ultra-mobile personal computer (UMPC), and/or any other device that is equipped to communicate over a wired and/or wireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments, one or more member devices within member devices1802-1804may include may run one or more applications, such as Internet browsers, mobile applications, voice calls, video games, videoconferencing, and email, among others. In some embodiments, one or more member devices within member devices1802-1804may be configured to receive and to send web pages, and the like. In some embodiments, an exemplary specifically programmed browser application of the present disclosure may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web based language, including, but not limited to Standard Generalized Markup Language (SMGL), such as HyperText Markup Language (HTML), a wireless application protocol (WAP), a Handheld Device Markup Language (HDML), such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, and the like. In some embodiments, a member device within member devices1802-1804may be specifically programmed by either Java, .Net, QT, C, C++ and/or other suitable programming language. In some embodiments, one or more member devices within member devices1802-1804may be specifically programmed include or execute an application to perform a variety of possible tasks, such as, without limitation, messaging functionality, browsing, searching, playing, streaming or displaying various forms of content, including locally stored or uploaded messages, images and/or video, and/or games.

In some embodiments, the exemplary server1806or the exemplary server1807may be a web server (or a series of servers) running a network operating system, examples of which may include but are not limited to Microsoft Windows Server, Novell NetWare, or Linux. In some embodiments, the exemplary server1806or the exemplary server1807may be used for and/or provide cloud and/or network computing. Although not shown inFIG.20, in some embodiments, the exemplary server1806or the exemplary server1807may have connections to external systems like email, SMS messaging, text messaging, ad content providers, etc. Any of the features of the exemplary server1806may be also implemented in the exemplary server1807and vice versa.

In some embodiments, one or more of the exemplary servers1806and1807may be specifically programmed to perform, in non-limiting example, as authentication servers, search servers, email servers, social networking services servers, SMS servers, IM servers, MMS servers, exchange servers, photo-sharing services servers, advertisement providing servers, financial/banking-related services servers, travel services servers, or any similarly suitable service-base servers for users of the member computing devices1801-1804.

In some embodiments and, optionally, in combination of any embodiment described above or below, for example, one or more exemplary computing member devices1802-1804, the exemplary server1806, and/or the exemplary server1807may include a specifically programmed software module that may be configured to send, process, and receive information using a scripting language, a remote procedure call, an email, a tweet, Short Message Service (SMS), Multimedia Message Service (MMS), instant messaging (IM), internet relay chat (IRC), mIRC, Jabber, an application programming interface, Simple Object Access Protocol (SOAP) methods, Common Object Request Broker Architecture (CORBA), HTTP (Hypertext Transfer Protocol), REST (Representational State Transfer), or any combination thereof.

FIG.21depicts a block diagram of another exemplary computer-based system/platform500in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the member computing devices1902a,1902bthru1902nshown each at least includes a computer-readable medium, such as a random-access memory (RAM)1908coupled to a processor1910or FLASH memory. In some embodiments, the processor1910may execute computer-executable program instructions stored in memory1908. In some embodiments, the processor1910may include a microprocessor, an ASIC, and/or a state machine. In some embodiments, the processor1910may include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor1910, may cause the processor1910to perform one or more steps described herein. In some embodiments, examples of computer-readable media may include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as the processor1910of client1902a, with computer-readable instructions. In some embodiments, other examples of suitable media may include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. In some embodiments, the instructions may comprise code from any computer-programming language, including, for example, C, C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices1902athrough1902nmay also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, or other input or output devices. In some embodiments, examples of member computing devices1902athrough1902n(e.g., clients) may be any type of processor-based platforms that are connected to a network1906such as, without limitation, personal computers, digital assistants, personal digital assistants, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In some embodiments, member computing devices1902athrough1902nmay be specifically programmed with one or more application programs in accordance with one or more principles/methodologies detailed herein. In some embodiments, member computing devices1902athrough1902nmay operate on any operating system capable of supporting a browser or browser-enabled application, such as Microsoft™, Windows™, and/or Linux. In some embodiments, member computing devices1902athrough1902nshown may include, for example, personal computers executing a browser application program such as Microsoft Corporation's Internet Explorer™, Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In some embodiments, through the member computing client devices1902athrough1902n, users,1912athrough1912n, may communicate over the exemplary network1906with each other and/or with other systems and/or devices coupled to the network1906. As shown inFIG.21, exemplary server devices1904and1913may be also coupled to the network1906. In some embodiments, one or more member computing devices1902athrough1902nmay be mobile clients.

In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate in an cloud computing/architecture such as, but not limiting to: infrastructure a service (IaaS), platform as a service (PaaS), and/or software as a service (SaaS).FIGS.22and23illustrate schematics of exemplary implementations of the cloud computing/architecture(s) in which the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate.

In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary implementation of Neural Network may be executed as follows:i) Define Neural Network architecture/model,ii) Transfer the input data to the exemplary neural network model,iii) Train the exemplary model incrementally,iv) determine the accuracy for a specific number of timesteps,v) apply the exemplary trained model to process the newly-received input data,vi) optionally and in parallel, continue to train the exemplary trained model with a predetermined periodicity.

At least some aspects of the present disclosure will now be described with reference to the following numbered clauses.1. A method may include:receiving, by a machine-learning processor, an instruction to model at least one user-specific activity-specific engagement predicting score for at least one user from a plurality of users;obtaining, by the machine-learning processor, from a plurality of digital resources, based on the instruction, user-specific, activity-specific data;where the user-specific, activity-specific data may include:(i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user,(ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and(iii) at least one user-specific personal data of the at least one user;predicting, by the machine-learning processor, a user-specific activity engagement labeling data for the at least one activity by utilizing a first-type data pipeline on the at least one user-specific activity performance data;predicting, by the machine-learning processor, a plurality of user-specific, activity-specific data features by utilizing a second-type data pipeline on the user-specific, activity-specific data;predicting, by the machine-learning processor, based on at least one machine-learning model, the at least one user-specific activity-specific engagement predicting score, by utilizing:i) the user-specific activity engagement labeling data for the at least one activity andii) the plurality of user-specific, activity-specific data features; andinstructing, by the machine-learning processor, based on the at least one user-specific activity-specific engagement predicting score, at least one computing device to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.2. The method according to clause 1, where the predicting the at least one user-specific activity-specific engagement predicting score may include outputting a prediction utilization score by the at least one machine-learning model indicative of a likelihood that the at least one user will use a line of credit.3. The method according to clause 1, where the predicting the at least one user-specific activity-specific engagement predicting score may include outputting a prediction utilization score by the at least one machine-learning model indicative of a likelihood that the at least one user will churn a line of credit after given to the at least one user. (In other embodiments, the prediction utilization score may be indicative of a likelihood that the churning occurs within a predefined time interval.)4. The method according to clause 1, where the predicting the user-specific activity engagement labeling data by utilizing the first-type data pipeline may include using a times-series data pipeline to identify and to label at least one loan, at least one line of credit or any combination thereof used by the at least one user.5. The method according to clause 1, where the predicting of the plurality of user-specific, activity-specific data features may include using a feature data pipeline on the user-specific, activity-specific data with the user-specific, activity-specific data.6. The method according to clause 1, where the obtaining of the user-specific, activity-specific data with the at least one user-specific activity performance data regarding the performance of the at least one activity by the at least one user may include obtaining a loan data, a line of credit data, or both respectively of a loan, a line of credit, or both, that the at least one user used, churned, or both.7. The method according to clause 1, where the obtaining of the user-specific, activity-specific data with the at least one object data for the at least one object that allows the at least one user to perform the at least one activity may include obtaining at least one asset data for at least one asset that provides collateral for the at least one user to obtain a loan, a line of credit, or both.8. The method according to clause 1, where the at least one user is a single user, and where the instructing the at least one computing device may include instructing the at least one computing device to display a prediction utilization score indicative of a likelihood that the single user will use a line of credit.9. The method according to clause 1, where the at least one user is a single user, and where the instructing the at least one computing device may include instructing the at least one computing device to display a prediction utilization score indicative of a likelihood that the at least one user will churn a line of credit after given to the at least one user.10. The method according to clause 1, where the at least one user is a set of users from the plurality of users, and where the instructing the at least one computing device includes instructing the at least one computing device to display a prediction utilization score for each user in the set.11. The method according to clause 10, further including ranking, by the machine-learning processor, the prediction utilization score for each user in the set, and displaying, by the machine-learning processor, a ranking of the users based on the ranked prediction utilization score for each user in the set.12. The method according to clause 11, where the instructing the at least one computing device may include displaying recommendations for convincing the ranked users in the set to apply for a loan, a line of credit, or both.13. A system may include a memory and a machine learning processor. The machine-learning processor may execute computer code stored in the memory that causes the machine-learning processor to:receive an instruction to model at least one user-specific activity-specific engagement predicting score for at least one user from a plurality of users;obtain from a plurality of digital resources, based on the instruction, user-specific, activity-specific data;where the user-specific, activity-specific data may include:(i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user,(ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and(iii) at least one user-specific personal data of the at least one user;predict a user-specific activity engagement labeling data for the at least one activity by utilizing a first-type data pipeline on the at least one user-specific activity performance data;predict a plurality of user-specific, activity-specific data features by utilizing a second-type data pipeline on the user-specific, activity-specific data;predict based on at least one machine-learning model, the at least one user-specific activity-specific engagement predicting score, by utilizing:i) the user-specific activity engagement labeling data for the at least one activity andii) the plurality of user-specific, activity-specific data features; andinstruct based on the at least one user-specific activity-specific engagement predicting score, at least one computing device to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.14. The system according to clause 13, where the machine-learning processor is configured to predict the at least one user-specific activity-specific engagement predicting score by outputting a prediction utilization score by the at least one machine-learning model indicative of a likelihood that the at least one user will use a line of credit.15. The system according to clause 13, where the machine-learning processor is configured to predict the at least one user-specific activity-specific engagement predicting score by outputting a prediction utilization score by the at least one machine-learning model indicative of a likelihood that the at least one user will churn a line of credit after given to the at least one user. (In other embodiments, the prediction utilization score may be indicative of a likelihood that the churning occurs within a predefined time interval.)16. The system according to clause 13, where the machine-learning processor is configured to predict the user-specific activity engagement labeling data by utilizing the first-type data pipeline by using a times-series data pipeline to identify and to label at least one loan, at least one line of credit or any combination thereof used by the at least one user.17. The system according to clause 13, where the machine-learning processor is configured to predict the plurality of user-specific, activity-specific data features by using a feature data pipeline on the user-specific, activity-specific data with the user-specific, activity-specific data.18. The system according to clause 13, where the machine-learning processor is configured to obtain the user-specific, activity-specific data with the at least one user-specific activity performance data regarding the performance of the at least one activity by the at least one user by obtaining a loan data, a line of credit data, or both respectively of a loan, a line of credit, or both that the at least one user used or churned.19. The system according to clause 13, where the machine-learning processor is configured to obtain of the user-specific, activity-specific data with the at least one object data for the at least one object that allows the at least one user to perform the at least one activity by obtaining at least one asset data for at least one asset that provides collateral for the at least one user to obtain a loan, a line of credit, or both. The system according to clause 13, where the at least one user is a single user, and where the machine-learning processor is configured to instruct the at least one computing device by instructing the at least one computing device to display a prediction utilization score indicative of a likelihood that the single user will use a line of credit.20. The system according to clause 13, where the at least one user is a single user, and where the machine-learning processor is configured to instruct the at least one computing device by instructing the at least one computing device to display a prediction utilization score indicative of a likelihood that the at least one user will churn a line of credit after given to the at least one user.21. The system according to clause 13, where the at least one user is a set of users from the plurality of users, and wherein the machine-learning processor is configured to instruct the at least one computing device by instructing the at least one computing device to display a prediction utilization score for each user in the set.22. The system according to clause 22, where the machine-learning processor is further configured to rank the prediction utilization score for each user in the set, and to display a ranking of the users based on the ranked prediction utilization score for each user in the set.23. The system according to clause 23, where the machine-learning processor is configured to instruct the at least one computing device by displaying recommendations for convincing the ranked users in the set to apply for a loan, a line of credit, or both.