STP vs. NTP & A Peek Into 2023

by Amit Jnagal, on December 23, 2022 11:00:00 AM PST

A decade of running startups has taught me one thing about myself - I am wrong a lot of times. I have been so sure about having hired rock star team members who proved me wrong when they did not deliver. More than a few times, I was sure about some investors putting money in my ventures that ultimately did not sign the dotted line. I have personally lost a lot of money on companies that I thought would change the world. Life has taught me that I am wrong more often than I am right. 

Around this time last year, I wrote a post about how 2022 was going to be the year of No Touch Processing (NTP) for Infrrd. Fast forward 12 months, and I am delighted to report that on this one occasion, I was not wrong. Yes, 2022 was an amazing year for No Touch Processing. Infrrd delivered on this promise and led a lot of our customers to the land of AI certainty. This was largely thanks to our stupendous Research Team that kept delivering marvels. It was a rocky ride, but we did it!


While we had impossible technology to deliver in order to achieve no-touch processing, our biggest challenge turned out to be something else. There are a lot of solutions in the market that claim to deliver 90% straight through processing (STP). So, a lot of customers believed that they already had what we were pitching to them. But there is a huge difference between NTP and STP… massive! It was a big challenge explaining that to customers.

Allow me to elaborate.
Most solutions work on the concept of a “confidence score” for the data they read. They use this score to tell you that they are 90% sure that they have extracted the data correctly. You test it and see that most of the time when the confidence score is 90%, the system is correct. So you configure a rule that says when the confidence is 90%, go with what the system says. This is called Straight Through Processing. But there are two huge problems with STP:


Problem #1 - Where is my 10%?

You will need to refresh your 8th-grade Algebra books to understand the first problem. When a system tells you that it is 90% sure about the extracted data, it is also saying that there is a 10% chance that it is 100% wrong. Otherwise said, one in 10 documents that have a 90% confidence score could be completely wrong. Imagine if you took an important financial decision based on this data. For example, you paid out an invoice of $90,000 instead of $9,000! Or approved a mortgage loan for $3 million instead of $300,000.

So, customers usually do not make decisions based on STP documents. They still need teams to verify these documents manually. And that is a costly investment.

Problem #2 - STP and Semi/Unstructured Data

Second, STP rates of 90% only work for straightforward documents that fit into a template. As documents grow in variations from hundreds to millions, the 90% confidence rates do not hold. Now, I am not saying that no one provides 90% confidence for semi-structured data, but as documents become more complex, this 90% confidence becomes less reliable. So most customers use it for fixed format documents such as forms, etc.

Problem #3 - Document Level Accuracy vs. Field Level Accuracy

To understand this problem, you will need a piece of paper, a pen, and a scientific calculator. Ready? Here goes - if a document has 10 data points for extraction and each of them is extracted with 90% confidence, then what is the confidence level for the entire document?

If your answer is 90%, then you are wrong. Indulge me for a moment. The probability of two events happening at the same time is the product of their individual productivity. So, if event one has a 90% probability, it must occur with a second event that also has a 90% probability, which then combined probability for both of them happening together is:
0.9 X 0.9 = 0.81 or 81%

So, when you apply the same math to 10 fields in a document extracted with 90% accuracy each, then the result is:

0.9 X 0.9 X 0.9 X 0.9 X 0.9 X 0.9 X 0.9 X 0.9 X 0.9 X 0.9 = 0.3486 or 34.86%

Will you make financial decisions on a document with a 34% probability of being correct? 

It’s not likely.

No-Touch Processing

NTP addresses all these problems. NTP ensures that you get 100%, not 90%, not 99% reliable data that does not need human reviews. It works for semi-structured documents as well as unstructured documents. Basically, it is AI’s guarantee that it has done a really good job with the extracted data and that you do not need to look at it. We have put 7 years of fundamental research toward this concept, as well as have filed and granted several patents into making this happen. 

We started this year by delivering 18% NTP processing rates for our customers. That meant that every 18 documents out of 100 did not need any review and that our customers have taken financial decisions based on this extraction, without any human review. As I write this, a few of our customers have crossed the 57% NTP rate. In 2023, we will continue to make investments in AI research around reasoning and certainty to take this rate beyond 70% and eventually to our old friend - the beloved 90%.

Not too far out in the future, when you apply for a mortgage online and upload your documents, you will get a loan decision instantly because the AI has perfectly read your documents and there was no need to wait for a human review. Infrrd is on a mission to make that happen!

Happy Holidays and a Very Happy New Year, Everyone!


Topics:Intelligent AutomationAI ReadinessIntelligent Document ProcessingMachine LearningIDP InsightsAP OperationsIntelligent Invoice Matching

About this blog

AI can be a game-changer, but only if you know how to play the game. This blog is a practical guide to turning AI into real business value. Learn how to:

  • Make sense of complex documents and images.
  • Extract the data you need to drive intelligent process automation.
  • Apply AI to gain insights and knowledge from your business documents.

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