Intelligent data extraction, also known as intelligent data capture, AI-powered data extraction, AI-based data extraction, etc., is an emerging back-office technology. It’s designed to relieve the inefficiency of manual extraction and obsolete information capture technology. The technology is flexible, fast and economical.
What makes this technology 'intelligent' is the incorporation of AI algorithms. In the past, data capture technology relied on a collection of document templates that taught the program where to source information on the page.
Take OCR engines, for instance. It is perfectly straightforward to train the model that the transaction references are to the left of the transaction amount on a particular bank statement. But simple, visual-based technology fails to understand the meaning of the information it captures.
In contrast, intelligent data extraction can actively understand subtle nuances in the data on the page through contextualisation. For example, the algorithms can distinguish between ACH credits and debits on a bank statement, meaning the correct information can be captured, even from complex tables.
While financial services embrace AI-based data extraction, it also has many other valuable applications across multiple industries, such as healthcare, HR, retail, and more.
There are many benefits to AI data extraction, which can be condensed into three core categories:
Intelligent data extraction technology uses AI-based neural networks to emulate the same reasoning as a human operator— only it’s faster. With manual extraction, the average person will take a few minutes to extract from one A4 sheet of paper. In contrast, AI technology can extract data in a few seconds.
Some people assume since AI is a cutting-edge technology, it must be prohibitively expensive. Luckily, that’s not the case–many vendors price per page and offer guarantees designed to enable their customers to get the most value for their money.
Case in point, Evolution AI guarantees its clients their money back if they discover an error in captured data (under our managed service).
Eliminating the long and tedious process of manual data extraction will boost employee satisfaction.
One of our clients, Jessica O'Hare, from YouLend, told us its impact on her team: "They've really bought into doing this because they don't want to do this part of their job. Instead, they want to do the more interesting parts. They don't want to be sitting there extracting information from a bank statement."
While there are few potential pitfalls with intelligent data extraction, here are three worth noting:
Obviously, if extracting data from financial documents, protecting confidential data should be a priority. Using a vendor could be a cause for concern, as it means transferring client data outside of an organisation.
Data security doesn’t have to be a drawback with the extraction vendor per se, provided they have the appropriate certification. Implementing a watertight data sharing agreement with a vendor can prevent any issues from occurring later. Confirm also that the vendor has a reputable data certification - ISO27001, for example.
Not every employee will welcome AI with open arms. They may believe intelligent technology will make their roles obsolete.
With the right change management, it’s simple to integrate AI technology throughout your organisation. Curious about how to make the transition process smooth? In our latest webinar, our clients discussed how to best deploy and manage AI technology in your organisation.
The gist is that you can win over stakeholders by letting the data speak for themselves. Once people see the benefits of automating data extraction, it shouldn’t be difficult to win them over. We’ve certainly never met anyone who has enjoyed manual data entry.
The quality of AI technology will vary by vendor. Discover what to ask in the initial vendor meeting in our intelligent document processing guide.
Some believe integrating intelligent data extraction with pre-existing company architecture may be difficult. However, data extraction tools are only intelligent if they're fit for purpose—which includes seamless implementation.
One of the most popular integration methods is using an Application Programming Interface (API) to communicate between the data extraction software and the company system. However, other possible integration options include file transfer via SFTP or directly uploading the documents to the extraction interface. The right integration method will depend on your company's infrastructure.
But is it worth the transition?
That depends on your company. For many organisations, using employees to extract data is an ongoing part of their operations. Other companies may have resigned themselves to less efficacious data extraction software, establishing efficient workflows around the pitfalls of their extraction technology.
However, as AI technology advances, ‘future-proofing’ has become a priority for some companies. Sustainably automating back-office tasks, for instance, ensures that organisations can focus on more ambitious projects.
As AI technology continues to develop rapidly, intelligent data capture will become even faster, cheaper and easier to implement.
AI data extraction will also become even better at integrating with external sources—organising and enriching data into an immediately actionable format. For example, banks may use government data like Companies House to verify potential customers during KYC processes. Although many intelligent data extraction providers can offer these types of capabilities, incorporating them into commercially viable packages is still on the roadmap for some vendors.
To reap these benefits, opt for a vendor with an academic and commercial focus on developing their technology. After all, future-proofing is only effective if the technology is capable of continual development.
If working with a vendor, you should expect the process to be under 48 hours. The training process involves uploading training documents to the technology and correcting any errors. The result should be 99-100% accuracy.
Once fully trained, intelligent data extraction should deliver 99.5%+ accuracy. If uploading new document types or poor-quality images, you might expect an occasional error to occur. By clicking and editing the error, the AI engine will learn from its mistake - and never repeat it.
One way to guarantee complete accuracy is to use a managed service where an analyst corrects any outputted errors. For sensitive financial data, a managed service can provide a cost-friendly means to maintain complete accuracy.
As AI abilities continue to evolve, data extraction might be just the beginning of AI-powered data management strategies. Now, AI users are delving into its analytical abilities - spurring a wave of fear of obsoletion in financial services.
But using data extraction technology to complete simple structuring, validation and analytical tasks. For example, our financial statement solution, Financial Statements AI, can calculate ratios from balance sheets and income statements.
Since its founding in 2015, Evolution AI’s intelligent data extraction technology has revolutionised the workflow of global businesses for nearly a decade. To discuss your business's use case with one of our experts, please book a demo or email us at hello@evolution.ai.
