Implementations described herein relate to methods, systems, and computer-readable media to determine transaction specific parameters. In some implementations, a method includes receiving a plurality of data records, wherein each data record comprises a plurality of attribute values associated with a transaction, grouping the plurality of data records into two or more clusters, wherein each cluster is associated with a respective set of attribute values, identifying a respective set of rules for each cluster of the two or more clusters, loading the data records and the respective set of rules for each cluster into a respective cache, and analyzing, in parallel with a plurality of program threads that execute on a respective processor associated with the respective cache and without accessing an external memory, such that the cache provides data locality for the data records using the respective set of rules.

This disclosure describes, in part, techniques for automating transfers of funds from a deposit account of a user to a savings account of the user based on historical transaction data associated with the deposit account. Using historical deposits and withdrawals from the deposit account, a prediction may be made regarding deposits to, and withdrawals from, the deposit account that will occur in a future period. Based on that that information and a balance to maintain in the deposit account for the duration of the future period, the amount of funds to transfer from the deposit account to the savings account may be determined. The techniques may further include transferring the funds to the savings account once determined.

Provided are a payment method and system through generation of a one-time payment-only number of a real card linked with an application, wherein in order to make payment safely by generating a one-time payment-only number every payment without leaving card information of a user in a provider's payment terminal or by using the generated one-time payment-only number, when a real card is linked with an application installed on a user terminal and the payment terminal recognizes the real card, a user signature input window, and transaction information about payment performed with a one-time payment-only number generated by a card company server are displayed on a screen of the application of the user terminal, and payment is performed when a signature of the user is input to the signature input window.

Systems as described herein determine merchant enforced transaction rules. A determination server may receive transaction data associated with a plurality of merchants. The determination server may generate a histogram of payments associated with a merchant category and filter out transaction data having purchase amounts above or below a predetermined threshold. The determination server may determine a first average purchase amount associated with merchants in the merchant category and a second average purchase amount associated with each merchant in the merchant category. The determination server may determine user spending patterns and that a first merchant in the merchant category enforces one or more card-based transaction rules using machine learning models. After determining that a user device is proximately located to the first merchant, a notification indicating the one or more card-based transaction rules associated with the first merchant may be sent to the user device.

Particular embodiments receive, by at least one computing device of a payment service, a request to configure a goal to associate with a user account of a user, where the goal is associated with a condition that, when satisfied, causes an incentive to be associated with the user account. The at least one computing device generates a data object for tracking completion of the goal, where the data object is stored in a datastore by the payment service. The at least one computing device monitors, in near-real-time, at least one of managed transaction data associated with users of the payment service or interaction data associated with the user. The at least one computing device determines, based at least in part on comparing at least one of the transaction data or the interaction data to at least the condition, satisfaction of the condition.

Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.

Particular embodiments determine, at least in part by a computing device of a payment service, to associate a data object with a user account that is associated with a stored balance managed by the payment service. The computing device associates an amount of the data object with a distinct stored balance associated with the user account. The distinct stored balance is associated with a condition of use. The computing device monitors transaction data associated with users of the payment service. The computing device identifies, from the transaction data, a transaction associated with an identifier associated with the user account. Based on a determination that the transaction satisfies the condition of use, processing payment for the transaction using at least a portion of the distinct stored balance prior to using the stored balance.

Systems and methods for automated notification and resolution of trigger events are disclosed. According to one embodiment, a method for automated notification and resolution of trigger events may include: (1) monitoring, by a backend computer program, an account for a trigger condition; (2) generating, by the backend computer program, a voice notification for the trigger condition; (3) communicating, by the backend computer program, the voice notification to an electronic device associated with the account and a link to a network location to resolve the trigger condition; (4) presenting, by the backend computer program and at the network location to resolve the trigger condition, one or more resolution options; (5) receiving, by the backend computer program and at the network location to resolve the trigger condition, a selection of one of the one or more resolution options; and (6) executing, by the backend computer program, the selected resolution option.

A computer-implemented system and method includes determining, by a mobile device associated with a user, a location of the user, generating, by the mobile device, a code comprising a tokenized value for sending funds and the location of the user, transmitting, by the mobile device, the code to a point of sale (POS) terminal associated with a merchant as part of a mobile wallet transaction, and receiving, by the mobile device, an indication that the mobile wallet transaction has been completed.

Improvements to existing technologies associated with point-of-sale transactions and merchant ecosystems to, among other things, reduce in-person contact and, in some examples, improve the efficiency at which point-of-sale transactions are completed (i.e., reduce friction) are described. In some examples, such reduced in-person contact and/or improved efficiencies can limit transmission of infectious diseases. As such, techniques described are directed to modifying aspects of point-of-sale transactions such that they occur on different computing devices (e.g., customer computing devices instead of merchant computing devices), are automated, and/or occur at different times than with conventional point-of-sale transactions. Furthermore, in at least one example, techniques described can leverage a distributed, network-based merchant ecosystem—comprising multiple merchant computing devices and/or customer computing devices that are specially configured to communicate with a service provider—to facilitate social distancing, which can reduce in-person contact and, in some examples, improve the efficiency at which point-of-sale transactions are completed.