Infrrd AI Enabled Intelligent Document Processing for Complex Documents

Intelligent Automation Infrrd client wanted to implement a 100%   automated insurance   claim process. Such a process would give them a competitive advantage over other providers. After collaborating on the 100% question, we found the most critical bottleneck in need of a fix was  accurately extracting data  trapped in various  unstructured documents   and images. Infrrd could help them  automate data extraction   with a high degree of accuracy, the claim process could run using  straight-through processing  — with minimal human involvement. We removed the  data extraction  bottleneck for them…Today, they operate right on the cusp of 100%.  <READ MORE >

When receipts come in different sizes, layouts, and formats, the manual method can break down. Employees can grow bored doing repetitive tasks and become disengaged, leading to unexpected and costly errors.  Worse yet, errors can trigger rework that steals even more of your employee’s time and productivity.  This calls for a smart solution that can effortlessly automate repetitive tasks so employees can spend their time on things that truly matter, doing things that only humans can do well. Modern solutions can also increase efficiency and quality by automatically doing real-time policy checks, detecting duplicate and fraudulent bills, and more.  And the solution is…. Automation. But hold on, all data extraction solutions aren’t the same. For example, consider  OCR . It’s an old but reliable horse in the race of automation and many businesses use OCR today to extract data from documents. But OCR has its limitations - especially when it tries to tackle complex...

                                AI Technologies Someone has to pay the price for your AI failure. Let it be your competitors.  A back-office supporting the insurance industry processed millions of documents every month, relying on a small army of manual data entry specialists.  This manual process was expensive, time-consuming, and difficult to scale fast enough to accommodate their growth. Every time they hired a new employee, it took about a year to train them to the point at which they were “fully productive".This company needed an  AI solution  to automate this process.   Did your  AI  implementation fail in 2019? Find out how enterprise companies made spectacular recoveries after their AI failures.                           ...

Data analysis without data preparation it is a myth. Unless we feed the right data in a proper format, Machine Learning algorithms won’t be able to solve our problem. If we give one wrong input then we end up where we started. So it’s very important to understand what data preparation is and how one can do it. Data, in its original form, may have a lot of missing pieces or disarrangement. Through data processing, one can modify this raw information from a specific database to a format which is understandable and learn able by the machine. Mentioned below are the ways that, we at Infrrd, employ in preparing our data. Data selection: It is necessary first to identify the type of data we are going to be working with. One has to keep in mind whether the available data will be able to address an existing problem or not. We keep certain factors in consideration before selecting the data: Data should not be of low quality: Low-quality input= low-quality output. Dataset is not error-r...

Intelligent Data Capture Infrrd’s IDC   platform can effectively extract attributes from stamps in a shorter timeframe with better accuracy. It also holds the capability to extract data from stamps that are overlapped with signature or text. The module also optimizes the stamps by reorienting them to get accurate data. With all the AI-capabilities in place, the decision-making process is made easier with better business outcomes without compromising on quality.                                   READ MORE ...

When introduced,  Optical Character Recognition software  was considered to be a boon for businesses. Instead of manually transferring data from records, one could now simply extract data from them. Later they can enter it automatically into in-house systems. We can save hours of manual labor but there remains a caveat. Errors introduced during the extraction process remain in your system. Before we recognize and fix the errors it can stall the work for many hours. OCR scanners  are prone to fail when one tries to extract data from old documents. Either the fading of the print or the lack of contrast can lead the software to incorrect recognition of patterns. Additionally, most online  OCR software  is designed to work with English as a language. But not everybody uses English as their first language. If you travel to Europe, the script remains the same but the language differs. A smart engine OCR should be able to pick up this difference in language and ada...

WHAT IS OCR? Data entry can often turn tedious and inaccurate if done manually. But, you can avoid this cumbersome process with an automated optical character recognition software. What OCR does is, it uses machine learning to scan each character on a page individually. It allows the documents to upload as text documents instead of images. We could scan easily and promptly any kind of printed document, be it receipts, invoices, contracts, utility bills and much more with this solution. HOW IS INFRRD’S OCR DIFFERENT FROM TRADITIONAL OCR? Our  OCR solution  is different in two ways: We don’t use templates but NLP for recognizing entities in a document. This helps us identify business names, bank details, amounts, prices etc. irrespective of where they occur in the document. Read more  here  on why templates are a bad idea. We use Machine Learning to fix what traditional OCR’s cannot figure out, by building an automatic context around the...

When introduced, Optical Character Recognition software was considered to be a boon for businesses. Instead of manually transferring data from records, one could now simply extract data from them. Later they can enter it automatically into in-house systems. We can save hours of manual labor but there remains a caveat. Errors introduced during the extraction process remain in your system. Before we recognize and fix the errors it can stall the work for many hours. ​ OCR scanners are prone to fail when one tries to extract data from old documents. Either the fading of the print or the lack of contrast can lead the software to incorrect recognition of patterns. Additionally, most online OCR software is designed to work with English as a language. But not everybody uses English as their first language. If you travel to Europe, the script remains the same but the language differs. A smart engine OCR should be able to pick up this difference in language and adapt its data extraction algori...