# Technology Behind Straight Through Processing | How STP Works

Here is Part 6 of our STP series. The past five articles on STP have introduced: (1) the concept of attended and unattended automation; (2) why STP is important; (3)how to achieve high STP in document automation; (4) examples of what it looks like; and (5) how to accurately assess STP.

### Technical Side of STP: Confidence Scores and Thresholds

Delving into the underlying intelligent capture technology behind achieving STP requires examining confidence scores. In client engagements and in conversations with analysts and consultants, the topic of confidence scores almost always comes up.

Far too often, there is a lack of understanding of what confidence scores actual are and how they are used. A common question is something like, “do you display confidence scores to users?” This question reveals a fundamental lack of understanding so let’s dig deeper into some common misunderstandings and the realities.

### Confidence Scores are not Probabilistic

Why is displaying a confidence score not useful? Because a score by itself has no specific ability to communicate whether the associated data output is accurate or not. A confidence score of 80 does not mean that it “has an 80% probability of being correct.” So if this is the case, “what good are they?” you might ask.

First, let’s use an analogy. Suppose there was a test for which a student got 30 questions correct and they come to you to ask what the resulting grade is. Could you give them a grade? No, you’d likely ask how many total questions there were on the exam. Confidence scores are similar in that you need more context in order to make them useful. Instead of knowing the total number of questions on the exam, you need to understand the full range of confidence scores for any particular answer value.

#### 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. likely 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 answers by confidence score from largest to smallest. It might look something like this:

In this sorted view of “Invoice Total” answers, we can see that answers with scores below 55 are more likely incorrect while scores with 55 or above are 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 sizable set of output and perform this exercise for each data field. Using this example, a score of 55 doesn’t mean 55% probability of being accurate, it simply means that, based upon analysis, it is likely to be correct.