Abstract: In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.

Abstract: A clock data structure is introduced. An instance of the clock data structure can be attached to a billing data structure, such as a billing data structure that corresponds to a recurring payment. The instance of the clock data structure has a value that represents a frozen time—i.e., a specific time that does not change unless instructed to. The billing data structures to which the clock data structure is attached operate as if the real time is the frozen time on the clock. Since the time is frozen, users wishing to test the billing data structure can update their system configurations at specific times, and subsequently advance the time in the clock data structure instance to see the effects on those configurations, as well as on other parts of the system, including asynchronous messaging and processing, and processes that involve other entities.

Abstract: A method comprises: setting an first entry and an second entry in a database; receiving a first online transaction; responsive to determining a lock of the database is in progress, suspending the first online transaction; updating the first entry; removing the lock; receiving a second online transaction; responsive to determining that the first field is greater than zero, decrementing the first field by an amount indicated in the second online transaction; and responsive to determining the second entry is greater than zero, decrementing the second entry.

Abstract: A method for preventing race conditions in a database comprises: setting a first numerical entry and a second numerical entry in a database; receiving a first online transaction; responsive to determining a lock of the database is in progress due to a second received online transaction, suspending the first online transaction; removing the lock after the second received online transaction is completed; responsive to determining that the first entry is greater than zero, decrementing the first entry by an amount indicated in the first online transaction; responsive to determining the second entry is greater than zero, decrementing the second entry; processing the first transaction; and generating an online transaction record for the first transaction and storing the generated record in an online transaction database.

Abstract: In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.

Abstract: In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.

Abstract: A dock data structure is introduced. An instance of the clock data structure can be attached to a billing data structure, such as a billing data structure that corresponds to a recurring payment. The instance of the clock data structure has a value that represents a frozen time—i.e., a specific time that does not change unless instructed to. The billing data structures to which the clock data structure is attached operate as if the real time is the frozen time on the clock. Since the time is frozen, users wishing to test the billing data structure can update their system configurations at specific times, and subsequently advance the time in the clock data structure instance to see the effects on those configurations, as well as on other parts of the system, including asynchronous messaging and processing, and processes that involve other entities.

Abstract: In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.