Abstract: In some implementations, the techniques may include capturing an image that includes a depiction of an authorization card. The techniques may include providing the image as an input to the machine learning model. Moreover, the techniques may include receiving information that identifies the authorization card as output from the machine learning model. Also, the techniques may include comparing the information that identifies the authorization card to one or more criteria. Further, the techniques may include performing an operation based at least in part on the comparing of the information that identifies the authorization card to the one or more criteria. In addition, the techniques may include performing an operation based at least in part on comparing the information that identifies the authorization card to the one or more criteria.

Abstract: An example method may include receiving, from a mobile device, image information representing an image that includes a depiction of a media of exchange. The method may also include providing the image information as input to a machine learning model implemented by the remote computing device. The method may also include receiving, from the machine learning model, an output comprising a classification and a confidence score, the classification comprising a quantity that corresponds to the media of exchange, the confidence score comprising a probability that the media of exchange in the image corresponds to the quantity. The method may also include comparing the confidence score with respect to one or more evaluation criteria. The method may also include, in response to comparing, providing a user interface for presentation at the mobile device, the user interface comprising an option for establishing a communication channel.

Abstract: The techniques may include capturing an image that includes a depiction of the media of exchange. In addition, the techniques may include communicating image information representing the image to a remote computing device that is configured to execute a machine learning model that receives as input the image information and outputs a quantity that corresponds to the media of exchange. The techniques may include receiving a quantity that corresponds to the media of exchange and a confidence score that includes a probability that the media of exchange in the image corresponds to the quantity. Moreover, the techniques may include comparing the confidence score with respect to one or more evaluation criteria. Also, the techniques may include in response to the comparing, presenting, via a user interface, an option for establishing a communication channel. Further, the techniques may include in response to receiving input from the user interface, establishing the communication channel.

Abstract: The techniques may include detecting a repository value change for an account. In addition, the techniques may include receiving image information representing an image from a computing device that is associated with the account, where the image includes a depiction of the media of exchange. The techniques may include generating a feature vector for the image, where the feature vector is generated based at least in part on the image information, and where the feature vector may include numeric properties of the image. Moreover, the techniques may include associating the feature vector and the repository value change, thereby generating a labeled training datum. Also, the techniques may include adding the labeled training datum to a set of labeled training data. Further, the techniques may include using the set of labeled training data to train a machine learning model that is configured to receive as input the image information and output a quantity that corresponds to the media of exchange.