Abstract: Systems and methods are disclosed for performing real?time fraud detection during an in?progress call between a calling device and a called device over a telephone network. Call audio is monitored and evaluated by a filtering model that generates an indication of fraud. When the filtering model output indicates potential fraud, data describing the calling device and the call content are provided to a fraud detection machine learning model. The model generates a fraud detection value indicating a likelihood that the call is fraudulent. If the value exceeds a threshold, the called device presents a fraud alert.

Abstract: Various embodiments are directed to systems and methods for checking in at a business with a mobile computing device. For example, the mobile computing device may receive via a data network, location information for a venue. The mobile computing device may also determine whether a location of the mobile computing device is available. Conditioned upon the location of the mobile computing device being available, the mobile computing device may determine whether the location of the mobile computing device is within a threshold distance of the venue. Conditioned upon the location of the mobile computing device being within the threshold distance of the venue, the mobile computing device may send a check-in message to a server. The check-in message may indicate that the user of the mobile computing device is at the venue.

Abstract: Various embodiments are directed to systems and methods for determining a neighborhood location of a person using a mobile computing device. The mobile computing device may transmit its location to an application server system. The application server system may perform at least one search of a venue database over a plurality of venue categories considering the received location and may determine a neighborhood location of the mobile computing device based on results of the search.