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: A feedback instrument management system may be used to send a feedback instrument based on a user interaction. The user interaction and one or more feedback instruments may be received at a processing module. The feedback instruments may each have an associated set of rules and limiting criteria. The processing module may determine which feedback instruments have all rules satisfied and have not met the limiting criteria. A feedback instrument from the set of feedback instruments that have all rules satisfied and have not met the limiting criteria may be selected. The selection may be based on a priority value or an auction. The auction may be based on auction factors, such as satisfaction scores. The selected feedback instrument may be sent to the user.