Abstract: An Artificial Intelligence (AI) based payments processing system analyzes data from a user's purchase invoice to identify a combination of payment modes to be used to pay for the user's purchases thereby maximizing the savings to the user while accounting for the user preferences. A savings oriented model including a mix and match model and a dynamic decision tree scoring model generates a savings based best offer suggesting the payment modes to be used by analyzing the user's purchases based on the offers associated with the purchased products. A user behavior model generates a user behavior based best offer by further accounting for user preferences. Based on the user's autopay settings, the payments processing system may automatically initiate payment for the user's purchases or may display the best offers to the user before initiating the payments.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving, from a video monitoring system, a count of people detected at a first location, wherein the video monitoring system comprises a camera configured to capture video of the first location, receiving a count of wireless devices detected in a second location, and applying a first correction factor to the count of wireless devices detected at the second location to determine a number of people present in the second location, wherein the first correction factor is based on the count of people detected by the video monitoring system and a count of wireless devices in the first location.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving, from a video monitoring system, a count of people detected at a first location, wherein the video monitoring system comprises a camera configured to capture video of the first location, receiving a count of wireless devices detected in a second location, and applying a first correction factor to the count of wireless devices detected at the second location to determine a number of people present in the second location, wherein the first correction factor is based on the count of people detected by the video monitoring system and a count of wireless devices in the first location.