The volume of data generated worldwide is growing rapidly, with a Statista chart predicting a volume of 181 zettabytes by 2025. What doesn’t make companies happy is the fact that, according to IDC, 80 percent of this data is expected to be so-called unstructured data in 2025, i.e. text or image files, PowerPoint presentations, e-mail messages, completed forms and the like, and that their share of the data volume will continue to grow. The problem with them is that they cannot be stored in databases without preparation and therefore cannot be taken into account when companies want to digitize and automate processes.
Large companies offer their customers self-service portals for capturing data and making changes; large insurance companies and health insurers started using OCR-based solutions for scanning, classifying, extracting and archiving information from documents years ago. In companies that could not and cannot afford such capture lines, employees have to manually transfer the information from delivery bills and invoices, proofs of identity such as ID cards, contract documents, application forms into their system.
From unstructured to structured data
Intelligent Document Processing (IDP) frees companies from this error-prone, time-consuming routine work and analyzes unstructured or semi-structured information and even handwritten documents and converts them into structured data quickly, reliably and largely error-free. To do this, the technology combines OCR with artificial intelligence techniques such as machine learning, deep learning, and natural language processing (NLP); it understands the context of the data from the documents. Compared to OCR, IDP has the advantage that the technology does not require templates, learns from the processed data, and continuously improves recognition accuracy.
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The technology accelerates capture processes in companies of all sizes and industries, in manufacturing companies, in the service sector, in banks and insurance companies, or in public administrations, and does so without tedious and error-prone routine work. Hardly any time passes before the incoming invoice is entered in the company. It can be processed immediately by the accounting department, so that no discount period is missed. Applications are quickly available. Employees can access and process them immediately, and customers no longer have to wait days or weeks to receive a response. This pays dividends in terms of service quality and improves customer loyalty. It also makes it very easy to capture data that was previously ignored. It can be analyzed, for example, with a view to targeting customers more effectively or expanding the range of services.
Basis for fully automated processes
Once the data is captured as structured data, it can not only be analyzed but also used by other technologies. IDP thus provides the basis for further automation. RPA (Robotic Process Automation) comes into question for this. The data from the incoming invoice, for example, can then be further processed not only by employees, but also by programmable software bots. They take over computer-aided, standardized processes with fixed rules and procedures: They calculate the cash discount, deduct it from the invoice amount and transfer the remaining amount completely automatically.
Conclusion
IDP can certainly be described as an entry-level technology in several respects. As a ready-made AI solution, IDP places only low demands on the companies using it and is therefore suitable for companies that have little experience with AI solutions to date. Taken on its own, the structuring and collection of data already offers considerable added value for companies, as lengthy and error-prone collection processes are no longer necessary. At the same time, IDP is the starting point for further uses of the data. In a next step, previously unused information can be tapped from the captured records and used to improve offerings and services. As a third step, work processes can be fully automated on the basis of IDP using RPA.
