Straight Through Processing - Document Automation A Practical Example: Invoice Data Extraction For instance, let’s suppose we have 1000 invoices that we use to configure and measure a system. For this project, we wish to use confidence scores to determine when the “Invoice Total” field is likely to be accurate vs. inaccurate. Rather than evaluate a single “Invoice Total” confidence score from the thousand, we evaluate the scores for all 1000 invoices. Doing this, we can evaluate what “Invoice Total” answers are correct and which ones are incorrect, noting the confidence score ranges for the correct and incorrect answers. We can then order the answersbyconfidencescorefromlargestto smallest.It mightlooksomethinglikethis: Answer Correct? Score 102.70 Y 78 95.00 Y 72 34.23 Y 65 54.36 Y 55 28.55 N 35 250.75 N 28 136.12 N 10 In this sorted view of “Invoice Total”answers,we canseethatanswerswith scoresbelow55aremorelikelyincorrectwhilescoreswith55oraboveare likely correct. Of course the total sorted list would consist of 1000 answers, but you get the point. The only way to use confidence scores is to take a sizablesetofoutputandperformthisexerciseforeachdatafield.Usingthis example, a score of 55 doesn’t mean 55% probability of being accurate, it simplymeansthat,baseduponanalysis,itislikelytobecorrect. 29