Abstract: In an example embodiment, a machine learning training pipeline is introduced that continuously monitors and processes training data having multiple transaction types using a sliding window, adding labels as they are available for the various different types of transactions in the training data. The processed training data, with the appropriate labels added, can then be utilized by any machine learning model that is being onboarded using the pipeline, without any specialized setup being necessary. Further, even if new data is added to the pipeline to aid in the training of a new model (such as data regarding a new payment type), this new data can be processed quickly and added to the existing data without requiring specialized processes by the entity requesting the new machine learning model. This allows the actual training of the new machine learning model to be accomplished very quickly, and deployment to be accomplished even faster.

Abstract: In an example embodiment, a solution is provided wherein a single machine learning model is trained and used to detect fraud across multiple payment types. More particularly, the concept of a payment scoring event (PSE) is introduced. A PSE is a data structure that stores multiple pieces of information about a transaction (or potential transaction). A mapping is then maintained between each payment type to be supported and the PSE structure. Each of these transaction types may have its own mapping indicating which fields in the PSR its own fields map to. The use of these mappings allows for additional payment methods to be introduced and supported at any time, necessitating only the creation of a mapping for an additional payment method.

Abstract: A method of conserving power for a navigation application of a device is provided. The method determines a time for timely departure to an event that is stored in a calendar application of the device. The time for timely departure is determined based on the starting time of the event and an estimated travel time from the current location of the device to the location of the event. The method provides the time for timely departure to the calendar application. The method calculates the time for providing an update to the time for timely departure based on the remaining time before the time for timely departure. The method hibernates the navigation application until the time for providing the update. The method repeats the determining, providing, calculating, and hibernating several times.

Abstract: A method of conserving power for a navigation application of a device is provided. The method determines a time for timely departure to an event that is stored in a calendar application of the device. The time for timely departure is determined based on the starting time of the event and an estimated travel time from the current location of the device to the location of the event. The method provides the time for timely departure to the calendar application. The method calculates the time for providing an update to the time for timely departure based on the remaining time before the time for timely departure. The method hibernates the navigation application until the time for providing the update. The method repeats the determining, providing, calculating, and hibernating several times.

Abstract: A method of providing travel updates for an event stored in a calendar application of a device is provided. The method receives the location and the starting time of the event from the calendar application at a navigation application of the device. The method determines an estimated travel time from the current location of the device to the location of the event. The method determines a time for timely departure to the event based on the estimated travel time. The method provides several travel updates that include the time for timely departure to the calendar application. The travel updates are utilized by the calendar application to provide a set of alerts regarding the time for the timely departure to the event.