Self-service terminal (SST) predictive error analysis

A method, comprising:

• training a machine learning model (model) on telemetry data specific to currency peripheral operations and service records associated with media peripherals of a terminal to predict error codes that are likely to occur but have not yet occurred on the media peripherals by training the model to identify patterns and relationships between error codes and sequences of the error codes using telemetry data and based on service records, wherein the patterns identify when media peripherals will be unable to accept or dispense currency;
• providing current telemetry data as input to the model;
• receiving a predicted error code for a certain media peripheral as output from the model, wherein the predicted error code includes a future time period during which the predicted error code is expected to occur;
• and
• scheduling a service call to address the predicted error code on the certain media peripheral with a maintenance and support system;
• wherein scheduling further includes:
• piggybacking the service call that is scheduled with an existing service call already scheduled for a store where the terminal is located within the maintenance and support system when the existing service call is scheduled to occur before the future time period.

A system, comprising:

• at least one server comprising a processor and a non-transitory computer-readable storage medium;
• the non-transitory computer-readable storage medium comprises executable instructions;
• and
• the executable instructions when executed on the processor cause the processor to perform operations comprising:
• using telemetry data associated with media peripherals of a terminal to predict an error code that has not yet occurred on a certain media peripheral of a terminal by training a machine-learning model on the telemetry data and service records specific to media peripheral failures to predict error codes and sequences of error codes that will render a particular media peripheral unable to accept or dispense currency, and providing the telemetry data as input to the machine-learning model;
• linking the error code to a knowledge article that provides descriptive steps for resolving the error code on the certain media peripheral;
• generating a service record that includes the error code and a predicted time frame when the error code will occur, and scheduling a service call to a certain media terminal to resolve the error code in advance of the error code being reported from the terminal;
• providing a link to the knowledge article within the service record;
• and
• determining whether the error code is associated with a hard fault that prevents currency transactions or a soft fault that does not prevent currency transactions, when the soft fault is identified determining when to optimally schedule the service call, and when the hard fault is identified determining to immediately schedule the service call.