Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize a plurality of data validation processes to merge or replicate data across databases for downstream operations. For example, in response to a request to merge data from one or more source servers to one or more destination servers, a disclosed system determines database events to merge based on a plurality of database tables accessed in connection with a card account transaction. The disclosed system validates subsets of database events for the accessed database tables based on the quantities and event identifier sequencing of the database events.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize a plurality of data validation processes to merge or replicate data across databases for downstream operations. For example, in response to a request to merge data from one or more source servers to one or more destination servers, a disclosed system determines database events to merge based on a plurality of database tables accessed in connection with a card account transaction. The disclosed system validates subsets of database events for the accessed database tables based on the quantities and event identifier sequencing of the database events. Additionally, the disclosed system replicates the database events from the source servers to the destination servers in response to validating the completeness and ordering of the database events for the card account transaction.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.

Abstract: Methods and systems are presented for predicting a likelihood of an occurrence of an event, such as a dispute or a chargeback, associated with an electronic transaction based on monitoring user interactions of a user with one or more computing systems after the electronic transaction has been conducted. The electronic transaction may involve a purchase of a product and/or service from a merchant. After the transaction is conducted, user interactions of the user with the one or more computer systems, such as a website of the merchant involved in the transaction, a website of another merchant, and a website of a payment service provider may be monitored. A machine learning model may then be used to predict whether the event associated with the transaction will occur in the future based on the monitored user interactions.