Straight Through Processing - Document Automation Rethinking Traditional RPA Approaching document automation requires a different level of thinking than “traditional” RPA automation. First, the notion that you can achieve a pass-fail at the page or document level for activities that involve automationofdataentryisnotpossible.Evenwithtoday’sadvancesin machinelearning,theprobabilitythat any single page will enjoy 100% accurate data location and extraction is very low: less than 5%. Only with page or document-level classification (where no document separation is involved) can you apply such a “binary” approach. For data extraction where there are multiple chances of success or failure on a single page,automationmustbemeasuredatadatafieldlevel,notatthepageordocument level. As an example, let’s take a flexible spending account process where the goal is to automate the location and extraction of five key data fields on receipts of various sizes and formats. If we expect a high percentage of receipts where all the data is processed accurately, then this would be the wrong approach. Even the most refined deep learning algorithms would have problems reliably locating and outputting all five fields on a majority of receipts. Maybe 5% to 10%of receipts wouldhavecompletesuccess.However,itcouldlocateand output 85%orgreaterofallfieldsofallreceipts. It looks something like this (these numbers are hypothetical, butbasedonrealtests): Successful Receipt-Level Extracton on: Outcome 1Field 98+% 2Fields 85+% 3Fields 50+% 4Fields 25+% 5Fields <15+% So looking at any data entry automation project in terms of a full page or document is not ideal because most pages or documents will require some level of review.