Abstract: Aspects of the subject technology include obtaining a transfer request event associated with a transaction, the transfer request event indicating a request day of week and time of day, and, when the request time of day is past a pre-determined cutoff time, adjusting the request day of week to be a subsequent day. Aspects also include determining, using the transfer request event and based on a machine learning model trained on historical transaction data, a predicted transfer delay for the transaction, obtaining a set of relevant non-transfer days based on a comparison between the historical transaction data and a set of past non-transfer days, and, when one or more non-transfer days from the set of relevant non-transfer days occur within a time period from the request day of week and over the predicted transfer delay, adjusting the predicted transfer delay based on the one or more non-transfer days.

Abstract: Aspects of the subject technology include determining an association between a first and second user account that comprises a first condition corresponding to a first action to be performed in association with the first user account and a second condition corresponding to a second action to be performed in association with the second user account. Aspects also include generating a first entry based on the association, receiving a first indication indicating completion the first and/or second actions, generating a second entry based on the first indication, wherein the second entry corresponds to the first and/or second conditions, determining whether the first and second condition of the first entry are satisfied based in part on entries in the data structure that correspond to the first and/or second conditions, and transmitting a second indication that the first entry is complete, in response to determining that the first and second condition are satisfied.

Abstract: Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Abstract: Some embodiments involve systems and methods for facilitating authentication for an entity system to use a partner system using pooled identifiers (e.g., without requiring the entity system to first register with the partner system). In some embodiments, based on (i) a request, from a first entity system, indicating a first partner system to process at least a part of an operation and (ii) an indication that the first entity system is not registered for use of the first partner system, a platform system may link a first authentication identifier of the first identifier set with the first entity system. The platform system may process the operation using the first authentication identifier linked by the platform system with the first entity system, and the platform system may use platform authentication data established with the first partner system to authenticate the platform system to the first partner system for performing the operation.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud.

Abstract: A transaction is conducted between a merchant site and a customer's electronic device using a payment processor. The merchant site is associated with a client-side application and a server-side application. The client-side application executes on the customer's electronic device. The client-side application electronically sends payment information retrieved from the customer's electronic device to the payment processor. The client-side application does not send the payment information to the server-side application. The payment processor creates a token from the payment information sent by the client-side application. The token functions as a proxy for the payment information. The payment processor electronically sends the token to the client-side application. The client-side application electronically sends the token to the server-side application for use by the server-side application in conducting the transaction.

Abstract: A system may include a network infrastructure having a set of network component nodes, each network component node configured to communicate with at least one other network component node in accordance with a dependency protocol; and a server in communication with the network infrastructure and a fault injection server. The server can be configured to monitor outputs generated by the network infrastructure and attributes of data communication between the set of network component nodes; execute a computer model using the dependency protocol and the monitored attributes and outputs as input to predict a set of faults; in response to presenting the set of faults for display on a user interface, receive a selection of one or more of the set of faults; and instruct the fault injection server to execute a fault injection scenario simulating performance of the network infrastructure operating under the selected one or more faults.

Abstract: In an example embodiment, a random forest machine learning algorithm is used to create and/or identify rules to apply to an individual entity in a computer system that has a plurality of entities, each with a number of rules. More precisely, rule predicates are used as features of a random forest model built to predict a particular outcome (e.g., a transaction that is fraudulent). Hyperparameters of the random forest model are varied and iterated. A classifier is used to calculate feature importance for all features in the training data. Feature importance may be calculated using permutation feature importance. The N “most important” features are then found from this set. The N “most important” features are then used to find rules above a certain precision and recall rate. These rules may then be backtested and the best rules can be used to generate additional rules.

Abstract: A server can adjust access privileges of a customer object of a first entity to provide access to the customer object to a second entity that is associated with the first entity in an organizational data structure, resulting in a shared customer object. An operation request can be received which is associated with the second entity with regard to the shared customer object. An option to associate at least one account object of the first entity to the shared customer object may be provided. After receiving an indication to associate the at least one account object to the shared customer object, the operation request may be executed using the at least one account object.

Abstract: Aspects of the subject technology include receiving a request to generate an event-based workflow associated with a first event type, which includes a state machine that includes at least a state and an associated transition and receiving an event including a second event type and an event attribute. Aspects also include determining whether the event attribute satisfies the triggering condition of the transition associated with the state, in response to determining that the second event type corresponds to the first event type, and when a current state of the event-based workflow corresponds to the state. Aspects also include performing the transition action of the transition and updating the current state based at least in part on the performed transition action, in response to determining that the event attribute satisfies the triggering condition of the transition associated with the state.

Abstract: A contactless card reader comprises a contactless card reader front-end coupled to a processor. A communications module is coupled to the processor and a set of sensors is coupled to the processor. The set of sensors determines parameters related to the location, orientation and motion of the card reader. The processor receives the parameters from the set of sensors and utilizes the parameters and scenario configuration data to evaluate a rule. The result of the evaluation of the rule results in a limitation on the operation of the card reader. The communications module is configured to intermittently receive the scenario configuration data from external sources.

Abstract: The present disclosure is directed to handling deployment of assets (e.g., applications) to devices (e.g., card readers, payment terminals, etc.), substantially as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims.

Abstract: The methods and systems disclosed herein allow for faster and more efficient authentication using a partial cookie instead of a full cookie (or other data structure). In one example, a server receives, during the first browser session at the first time, a first request for authorization from an electronic device along with authentication information. Responsive to generating a profile using the authentication information, the server transmits to the electronic device a first data source configured to grant access to the profile to the electronic device, via a first authentication protocol; and receives, at a second browser session at a second time, from the electronic device, a second request for authorization to access the profile; responsive to a determination that the electronic device includes the first data source, the server executes a secondary authentication protocol.

Abstract: In an example embodiment, a system is provided that allows for multiple service partners to be accessible simultaneously via a single API. The system ensures that requests to collect payment information are sent only to a service partner that is active and operating, ensuring that a request to collect payment information that would normally have been sent to a service partner that is down or otherwise unavailable will be rerouted to an alternative service partner. Furthermore, the system also allows for a dynamic selection of a service partner to utilize among multiple service providers, based on factors such as how well the service provider's system integrates with the underlying institution from which the payment will be made, and the estimated speed of the processing of the transaction by the service provider. This latter aspect can be technically challenging because the same institution may be known by multiple different names on the different service providers.

Abstract: A method and apparatus for detecting fraudulent merchant activities at a commerce platform system are described. The method may include accessing a first set of merchant system data and encoding the first set of merchant data into a set of structure data input signals. The method may also include accessing a set of time based data associated a set of sequential operation performed at the commerce platform system in response to the merchant system transaction being processed, and encoding the set of time based data into a set of unstructured time-based data input signals. The method may also include inputting the sets of inputs into different machine learning models to generate different machine learning model scores predictive of merchant fraud.

Abstract: A method comprising: creating, on a server, an object corresponding to the transaction; tracking state of the object as the object transitions between a plurality of states, the object being in one state while waiting for a transfer for the transaction; and automatically associating with the object by matching a reference code received with the transfer.

Abstract: In an example embodiment, a solution is provided that introduces a dynamic user interface that automatically identifies payment methods available for a transaction. The dynamic user interface also may optionally present the available payment methods in an ordering, as determined by a machine learning model. More particularly, a payment processing system may determine a payment intents object, with all available payment methods for a transaction and optionally an ordering for those payment methods.

Abstract: A card detection subsystem for control of a payment card reader, where the payment card reader comprises a magnetic stripe reader (MSR), an integrated circuit card (ICC) reader, and a near field communications (NFC) reader, and the NFC reader comprises an antenna. The card detection subsystem includes a processor, a storage, and one or more sensors. The one or more sensors detect a payment card and the antenna is either turned on or turned off based on the detection of the payment card.

Abstract: In some embodiments, a multi-model approach may be utilized to produce predictions with greater accuracy, which may then be used to generate content for one or more entities. In some embodiments, such models may include one or more machine learning models, heuristic models, exponential smoothing models, and/or other models. As an example, based on a processing volume corresponding to user data associated with a user, a first model may be selected for processing the user data over use of a second model for processing the user data. As such, based on the selection of the first machine learning model, the user data may be inputted into the first machine learning model to obtain a predicted result related to the user. Based on the predicted result, content for the user may be generated for display on a user device associated with the user.

Abstract: The subject technology receives a first request to deploy a first machine learning model, the first request including information indicating an identifier associated with the first machine learning model. The subject technology determines, based at least in part on the first machine learning model, a set of dependent machine learning models. The subject technology selects a first container environment to deploy the first machine learning model and the set of dependent machine learning models. The subject technology, based on the selected first container environment, executes an instance of an application in the selected first container environment, the first machine learning model and the set of dependent machine learning models being deployed as part of the instance of the application.