Straight Through Processing - Document Automation STP Approach Based on Data Science Unlikerules-basedtask automation,document-orientedtaskautomationtypicallyinvolvesmoresophisticated technologies including computer vision, classifier algorithms and pattern matching. While none of these technologies can deliver 100% certainty, well-designed systems can achieve predictability. This allows organizations to statistically measure results of particular configurations such that the output is reliable with high levels of tolerance with typically 95% accuracy or greater. System predictability is based on the careful curation of sample data, which is representative of the larger populationof productiondata.Usingthatset,thedataisanalyzedtoarriveattherightmixofalgorithmsused to create a particular task configuration. The results of the configuration are then analyzed against expected outputtoidentifythe“signature”ofaccuratedata.Data Science. Fromthere, the system determines — at accuracy levels that reach 99% — correct data from incorrect data. This means that even more complex “cognitive tasks” can benefit from straight through processing at the discrete tasklevel,savingorganizationsmillionsofdollarsevery year. In order to truly benefit from automation, organizations need as much straightthrough processing as possible. This requires significantly minimizing the need for any review whatsoever. To do that, success depends on reliably identifying correct output from the stuff that requires manual intervention. As with a self-driving car, noonewantstoconstantlytellaself-drivingcarwhattodo. How Much Straight Through Processing Can We Expect in DocumentAutomation? In the previous section, we discussed how approaching complex automation tasks such as document automation requiresa data science approach to create a system that has predictable and reliable output. It is not simply a case of 100% pass or fail. Rather, there are subtleties that have to be addressed. 13