Abstract: Adding an instrument to a digital application comprises a processor for training a machine-learning process based on historic data related to interactions of an instrument with counter-parties and users. The processor receives a request to add the instrument to a digital application associated with a user and accesses data associated with the instrument, the user, and the digital application. The processor enters the accessed data into the machine-learning process and determines a risk score of adding the instrument to the digital application based on the machine-learning process. If the processor determines that the risk score is less than or equal to a configured threshold, the processor adds the instrument to the digital application. If the risk score is greater than the threshold, the processor rejects the addition of the instrument.

Abstract: Preventing fraud or misuse associated with payment instruments comprises a processor for training a machine-learning process based on historic data related to interactions of an instrument. The processor trains a machine-learning process based on historic data related to interactions of an instrument and instrument issuer with counter-parties and users. The processor receives a request to evaluate the instrument for a risk of fraud and enters the accessed data into the machine-learning process. The processor determines a first risk score based on the machine-learning process that is based on a likelihood that the instrument issuer will remit invoiced funds and a second risk score based on a likelihood that the instrument issuer will initiate chargebacks. The processor determines that a combination of the first and second risk score is higher than a configured threshold and instructs the requester not to interact with the instrument.

Abstract: Techniques for advertisement placement prioritization are described. In one or more implementations, a likely line of sight of a viewer of an event to a focus point of the event is determined. Then, a relative location of one or more display devices is identified in relation to the likely line of sight of the viewer. In addition, a priority level is assigned to respective display devices based on corresponding relative locations in relation to the likely line of sight. Further, various demographics and characteristics of audience members can be obtained to determine context-sensitive media content associated with the audience members to present via relatively higher priority display devices. Also, performances of event participants can be determined based on commentary analysis and used to determine context-sensitive media content associated with the event participants to present via the higher priority ad boards.

Abstract: Techniques for advertisement placement prioritization are described. In one or more implementations, a likely line of sight of a viewer of an event to a focus point of the event is determined. Then, a relative location of one or more display devices is identified in relation to the likely line of sight of the viewer. In addition, a priority level is assigned to respective display devices based on corresponding relative locations in relation to the likely line of sight. Further, various demographics and characteristics of audience members can be obtained to determine context-sensitive media content associated with the audience members to present via relatively higher priority display devices. Also, performances of event participants can be determined based on commentary analysis and used to determine context-sensitive media content associated with the event participants to present via the higher priority ad boards.