Problems related to document analysis and processing are one of the biggest challenges for any company.
For some time now, there have been "box solutions" on the market, i.e., software that allow reading, interpreting, and capturing information from physical documents. Still, these tools are often not so versatile. They require additional investments of time and personnel to fit what companies need.
Let's look at a simple example. Suppose a company has many suppliers, each with different billing formats. To streamline the payment process, you invest in software that reads the invoices and extracts relevant information to inform the payment process.
For this type of tool to work, all invoices should be in a similar format, which is not always the case, but what if one of the suppliers wholly and suddenly changes the design in which they print their invoices?
Although changes can be introduced, doing so requires time and staff capable of manually adjusting the software's parameters. The good news is that this, and other problems associated with document processing and analysis tools, are becoming a thing of the past.
Here's how automation and cloud services are the force that is transforming document analysis in different types of industries.
Harnessing the power of the cloud and AI
The realities of many businesses make programs that use OCR (Optical Character Recognition) technology an inescapable necessity. Thanks to cloud computing, over the last few years, these types of tools have begun to be strengthened through technologies such as artificial intelligence and machine learning.
Let's go back to the example of the tool used to read invoices. With tools such as Amazon Textract or Amazon Comprehend, it is possible to recognize and collect helpful information in documents such as emails, phone numbers, etc. Thanks to the power of the cloud, tools of this type can not only automatically learn to extract information regardless of variations in the physical support that contains them.
This ability to interpret and collect data reduces the time spent on some processes and makes it possible to improve the user experience directly.
You gain in time and personalization.
Now let's think about a bank. There, it is necessary to analyze a very high volume of information from customers who apply for credit products.
This volume of physical documents means that the bank constantly has to deal with thousands of documents and certificates that arrive in different formats, some with handwritten information that is difficult to read. It can also happen that at certain times of the year, the number of applications from customers increases so much that it puts a strain on the staff in charge of carefully analyzing the information that appears on the applications while doing their best to give users a quick and timely response.
In this scenario, a tool capable of extracting and verifying information can ease the operational burden on the organization, but that's just the beginning.
If you are interested in learning more about using data to offer better financial products, you can also read "Personalization of financial services: six myths and how to overcome them."
In the above case, for example, in addition to the relevant information for approving or not approving the loan, they would make it possible to collect a large amount of data that could be used to get to know users better and offer them highly personalized services.
Reduces the possibility of error
One argument against this type of tool is that they are not 100% infallible and that an error of interpretation, say, in a handwritten line of text, could mean denying a loan and, as a consequence of that failure, losing the customer.
The good news is that AWS tools can indicate the confidence level with which they were able to extract the information from a document field. Based on that confidence level, a manual review instance can be created to minimize the chances of error.
Similar examples can be found in any company where processes require processing documents.
The advantages and flexibility they include can also be seen in how these tools are paid.
You pay for what you use
Unlike "out of the box" tools that often require significant upfront investments, document processing and analysis tools, such as those from AWS, are paid per page processed.
Pay-as-you-go also allows you to experiment and ensure that the selected tool works to meet your organization's needs. Services such as AWS also include a free layer that can be used for testing and is ready to go straight out of the box and designed to solve specific issues.
Finally, it is worth noting that any company that has processes in which it is necessary to digitize or process documents has the possibility of saving time and reducing costs through these types of tools. The key is to approach them, keeping in mind that the more flexible they are, the better they will adapt to the particularities of each organization while positively impacting the user experience.
