Artificial intelligence has a myriad of uses, but one of the most common and oldest is to analyse documents. It commonly used because typically growing companies accumulate more and more documentation. The documentation contains useful data that needs to be processed in some way, and what a better way that to process automatically with higher accuracy and efficiency than AI?
When talking about “documents” these often include the following:
- Customer Orders
- Product reports
- Risk reports
- And many many more…
From accountancy to insurance, these types of data often become meticulous, repetitive and concentration sucking acts of labour that beg for automation and insight.
What is possible when analysing documents?
Data extraction is the most common use of AI in document processing. Usually it consists of using the AI to extract specific parts of the document to send elsewhere or consolidate for better use.
An example could be to pull out the dates of a number of documents to label to documents accordingly, this can be difficult as the data could be added in a number of formats eg dd/mm/yy or written in longhand. The AI could deal with this and very quickly extract that data from a huge range of documents in little to no time at all.
That data can then better be grouped, analysed or displayed to gain precious insights on the original set of documents that would have taken days to manually process.
The extracted data could also be used to power a smart search, eg lookup all documents that have a certain characteristic.
Another use of AI is to analyse the whole document and classify it, for example the document could be client based or internal, invoice or purchase order, project 1 or project 2.
Machine leaning is mostly used for this, either supervised, where the perimeters of what makes each document what get programmed; or un-supervised where the documents are analysed by the AI, which then provides a personalised machine able to ‘understand’ what makes each document fit in each class.
Typically this requires a ‘training’ set of the documents that have already be labelled, these can then be fed into the machine learning algorithm (neural network) to train the system on how to classify those documents.
Document processing is more an amalgamation of multiple processes that can be used to provide a more complex output, such as replacing a back office process, such as invoice processing. This type of system could use NLP (natural language processing). This is where the AI is focused at recognising or understanding the details that text could include, such as instructions or annotations of a document. Put this together with other machine learning systems it can provide a comprehensive solution to analyse any document
From here many things are possible, below are a list of common uses for a document analysis engine:
- consolidating into a single file or folder the same type of document
- logging the data from multiple documents into a single, accessible location
- flagging documents and sending email (or other) notification to the right user
- adding security limitations to certain documents
- send the document to the correct department or user
- provide analytics on the data extracted from a number of documents
- Smart search though large sets of documents
- and more
Because document analysis is such as common use for AI (one of the first) its range of applications is numerous. Regardless of the requirements or the details of an existing back office process, it is very likely that a AI system could be used to assist or improve what is existing.
With the extra pressure of lockdown due to Covid 19 (at the time of writing this), efficiency is more vital than ever. AI systems, used in the right way can add a vital edge in adding efficiency.
For more information on what is possible and how document analysis using AI can help you, contact Pro AI via email@example.com to find out more.
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