Discover Financial Services

A client device is configured to (i) load a set of virtual test card profiles, (ii) present a graphical user interface (GUI) that enables selection of an operating mode for use in testing a payment terminal and a virtual test card profile for use in testing the payment terminal, (iii) receive a user selection of an operating mode for use in testing the payment terminal and a user selection, from the set of virtual test card profiles, of a virtual test card profile for use in testing the payment terminal, and (iv) based on the user selections, selectively operate in either (a) a first operating mode in which the client device emulates a physical payment card in accordance with the virtual test card profile or (b) a second operating mode in which the client device configures a configurable payment card to operate in accordance with the virtual test card profile.

An example client device is configured to (i) display an interface for deploying a new data science environment at a computing platform, (ii) receive, via the interface, a user selection of (a) a given data science application from a list of data science applications that is presented by the interface and (b) one or more deployment configuration parameters from a set of deployment configuration parameters that is presented by the interface, (iii) transmit, to the computing platform, a first network-based communication comprising an indication of the user selection of (a) the given data science application and (b) the one or more deployment configuration parameters, and (iv) receive, from the computing platform, a second network-based communication comprising an indication that the new data science environment has been deployed based on the user selection of the (a) the given data science application and (b) the one or more configuration parameters.

A computing platform is configured to (i) obtain a first set of watchlist data entries for parties that appear on one or more watchlists and a second set of screened data entries for parties that are to be screened against the one or more watchlists; (ii) determine an initial set of candidate matches between screened data entries and watchlist data entries using a first comparison technique (e.g., a comparison technique that utilizes similarity scores of a first type), and (iii) determine a narrowed set of candidate matches between screened data entries and watchlist data entries using a second comparison technique (e.g., a comparison technique that utilizes similarity scores of a second type).

A computing platform is configured to (i) generate a set of variable coalitions by randomly sampling from a variable-dependent distribution of a input variables, (ii) identify a given input data record to be scored by a trained model object, (iii) generate a set of variable-independent synthetic samples (iv) execute the model object to output a score for each variable-independent synthetic sample, (v) for each respective input variable, (a) generate a variable-dependent set of synthetic samples, (b) execute the model object to output a set of scores for each variable-dependent synthetic sample, (c) evaluate a difference between the set of scores for each variable-dependent synthetic sample and the corresponding set of scores for each variable-independent synthetic sample, and (d) determine a set of iteration-specific contribution values for the respective input variable, and (vi) for each respective input variable, average the iteration-specific contribution values and thereby determine an aggregated contribution value.

An example computing platform is configured to (a) cause a client device associated with a user to display an interface for deploying a new data science model, where the interface presents the user with a list of deployment templates, and where each of the deployment templates includes data specifying (i) a respective executable model package and (ii) a respective set of execution instructions for the respective executable model package, (b) receive, from the client device, data indicating (i) a user selection of a given deployment template for use in deploying the new data science model and (ii) a given set of configuration parameters for use in deploying the new data science model, and (c) use the given executable model package, the given set of execution instructions, and the given set of configuration parameters to deploy the new data science model.

A computing platform is configured with new software technology for accurately identifying recurring charges and facilitating interaction with identified recurring charges, which may involve functionality for (a) obtaining data for a given transaction involving a given customer account and a given merchant, (b) applying pre-processing logic to the obtained data for the given transaction and thereby derive feature data for the given transaction, (c) inputting the feature data for the given transaction into a trained machine-learning model that functions to (i) evaluate the feature data for the given transaction and (ii) based on the evaluation, output a score for the given transaction that indicates a likelihood that the given transaction is a recurring charge, and (d) based on the score for the given transaction, determining whether to classify the given transaction as a recurring charge.

A computing platform is configured to (i) use a deposit limit model to determine a deposit limit for an account holder of one or more deposit accounts, wherein the deposit limit comprises a monetary limit on a cumulative amount of deposits that the account holder is permitted to make into one or more deposit accounts during a time window, (ii) receive, from a client device associated with the account holder, an indication of a request from the account holder to make a new deposit into a deposit account, (iii) determine whether the deposit limit for the account holder is violated by the new deposit, and (iv) either (a) accept the new deposit if the deposit limit for the account holder is not violated by the new deposit or (b) reject the new deposit if the deposit limit for the account holder is violated by the new deposit.

A computing platform is configured to (i) generate first and second sets of time-series models for forecasting values of a time-series variable for respective and different first and second timeframes, the first and second sets each comprising models of different types, (ii) (ii) receive configuration data for an ensemble model that identifies a user-selected group of time-series models to be included in the ensemble model, where the user-selected group includes at least one model from the first set and at least one model from the second set, (iii) based on the received configuration data, construct the ensemble model from the user-selected group of time-series models, where the ensemble model is configured to blend the forecast values of the time-series variable that are predicted by the user-selected group of time-series models, and (iv) utilize the ensemble model to predict a given sequence of forecast values for the time-series variable.

A computing platform may be installed with software technology for scoring and managing digital content that configures the computing platform to: (i) cause a client device to present a user interface for creating a scoring model for content hosted within a content management system (CMS); (ii) receive data defining a given scoring model that is configured to output a trustworthiness score for a piece of content based on an evaluation of data for a given set of input variables; (iii) use the given scoring model to evaluate a given piece of content hosted within the CMS by (a) obtaining data for the given set of input variables and (b) inputting the obtained data into the scoring model and thereby determine a given trustworthiness score for the given piece of content; and (iv) based on the given trustworthiness score for the given piece of content, manage the given piece of content.

A computing system is configured to (i) receive an event stream produced by at least one event producer; (ii) evaluate each respective event in the event stream against a set of one or more constraints; (iii) based on the evaluation, determine that a given event in the event stream is governed by a given constraint in the set of one or more constraints; (iv) after determining that the given event is governed by the given constraint, cause the event to be replaced by a corresponding placeholder event within the event stream, wherein the placeholder event omits at least a portion of data included within the given event; and (v) after determining that the given event is governed by the given constraint, cause the given event to be stored in an event repository that complies with the given constraint.