Straight Through Processing - Document Automation Designing& Measuring aSystem Confidence Score You cannot design and measure a system based andThresholds solely on the ability to achieve either a pass or This is where the concepts of “confidence scores” fail 100% of the time. This is unlike designing and “confidence thresholds” come into play. For automation for very rote, defined tasks for any advanced capture system, the output for each whichit is easy to determine pass/fail scenarios. answer will be accompanied by a confidence score. Whenitcomestoemploying“cognitive”typesof This score is based on a lot of inferences such as computing, we have to deal with predictions, complexity of the location, whether thereare other candidate answers, range of potentialanswersand not absolutes. Machine learning makesmistakes many more. that are hard to plan for or comprehend. The Confidence scoresare meant to be used to morecomplexthetaskis,themorecomplexthe construct a statisticalmeasure of all output so that potential outcomes. For any particular data field it is possible to create a boundary score, often on a page, there is a probability of a system’s called a confidencethreshold, which determines ability to achievesuccess. with a certain levelof precision whether output for a given field is correct or incorrect. Without confidences scores,we would have to manually review all output – that certainly doesn’t sound like straight throughprocessing. STP Measured at the DataFieldLevel The amount of straight through processing needsto be measured at the data field level and requires identification of confidence thresholds for each data field type in order to automatically identify good data frombad. This means that workflows employing document automation should emphasize review only on the data fields that require it. The remainder shouldbe shuttled straight into downstream processes. Overall benefits are measured not on how many pages or documents can move straight through, but how much overall data entry is removed from the equation. It stands to reason that if an organization is performing data entry on 100 million data fields per month, reducing 50% of that workload at a data field level represents significant value.