What is Intelligent Document Processing?
Data is the lifeblood of any business; however, converting all the flow of documents and files that reaches a company into useful information is not an easy task and makes a difference when it comes to competing. There are not a few companies that give up using the valuable information provided by the unstructured data they receive or, to do so, resort to manual extraction of this data.
This manual extraction should no longer be an option for organizations because, even for smaller ones, dealing with a smaller volume of documentation, ends up being inefficient. Fortunately, technology provides other alternatives.
What is Intelligent Document Processing?
The extraction of information has evolved very rapidly in recent years, coming to converge different technologies that present differences among themselves:
- OCR (Optical Character Recognition ). Optical Character Recognition essentially converts text scanned as an image into a machine-readable text file. Although it was a giant step forward from manual data extraction years ago, it has functional limitations, especially when documents are not based on simple templates or have variations in design. Also, it cannot extract any context from the extracted data nor is it suitable for unstructured and semi-structured or handwritten documents.
- RPA (Robotic Process Automation). Software bots represent an advance compared to OCR, since they allow automating tasks such as the capture of information to process a transaction, manipulate data, trigger responses, or communicate with other computer systems. However, it shares a limitation with OCR: it is highly efficient when the data is available in a structured format with little or no variation, but the rules it relies on for its performance are extremely sensitive to any changes in the structure of the content of the data. entry. This results in greater effort being required to maintain and scale these processes.
- IDP (Intelligent Document Processing). Unlike OCR, Intelligent Document Processing enables the capture of information from structured, semi-structured, and unstructured documents, which can range from email text to PDF or different scanned documents that are not subject to any template. Thanks to the incorporation of technologies such as Artificial Intelligence and Deep Learning, the IPD classifies and extracts the relevant data for further processing, significantly improving the process, even going so far as to validate the information and present it in the desired structured format. Thanks to AI, an IDP system continuously learns from data without any human intervention or rule configuration in the system.
Most common use cases
- Data capture and extraction. Thanks to algorithms and its continuous learning capacity, the IDP is capable of intelligently extracting information from a scanned image. In other words, it is not necessary for the data to be structured in CSV, XML, or JSON… formats, but rather it can identify specific data among unstructured data, such as an order number on a delivery note. In specific tasks such as mortgage or invoice processing, it is especially useful, minimizing errors and streamlining management.
- Document Classification and verification. Among the AI technologies used by the IDP is Natural Language Processing (NLP), thanks to which certain content is correlated with existing categories and allows the classification and verification of information. If the system scans a document, thanks to the algorithms it is capable of automatically recognizing that it is a receipt and not a payslip, for example. Thanks to this verification capacity, the IDP has become an ally in the fight against fraud, reducing the number of illegal financial transactions.
- Data anonymization. When certain files are processed, such as credit cards, medical reports, etc. It is possible that depending on the final purpose of this information capture, it is not necessary to collect sensitive information, such as personal data. Intelligent information processing is capable of automatically identifying this data and anonymizing it, preserving privacy and complying with current legislation such as the GDPR.
- Customer onboarding. Especially in sectors such as finance, the IDP is especially useful for carrying out what is known as KYC (Know Your Customer) processes, which basically consist of verifying that the client is who they say they are in order to give them access to certain products and services. . For this, the system processes identification documents such as ID, passport or driver’s license, among others.
Advantages of using IDP
- Reduced processing time. The speed with which an IDP system is capable of processing data is far from the manual processes that were established in organizations until very recently. It is estimated that it can lead to time savings of 90%, going from several minutes to classify and extract information to just a few seconds.
- Productivity increase. Added to the speed with which intelligent data processing occurs is the ability of this technology to take on huge volumes of data, doing so 24×7. As a consequence, productivity ratios skyrocket in companies, since all the processes dependent on this extraction and classification of information are streamlined. Along the same lines, freeing employees from these tasks also contributes to performing functions with greater added value for the organization.
- Precision. As in carrying out any repetitive task that becomes tedious, fatigue ends up making an appearance and, with it, the propensity to make mistakes increases, the consequences of which are exponential when we talk about managing information for other processes. Intelligent data processing yields up to 99% accuracy in these tasks, without fatigue and, on the contrary, constantly improving thanks to its machine learning functionality.
- Improve security. As we have pointed out in the use cases, the additional AI functionalities that distinguish IPD from OCR or RPA allow the system to guarantee compliance with personal data protection legislation, while its information verification capacity has been enhanced. become a key instrument in KYC and anti-money laundering (AML) processes.
- An IDP system can manage and process large volumes of data without the need to increase resources or hire more staff. This capacity is not only useful when facing a growth and business expansion strategy, but also during the seasonal peaks that some businesses have, such as retail, for example, during Black Friday or Christmas.
- Cost savings. Although the most important economic benefits are derived from increased productivity and the saving and release of employee time, IDP technology brings with it a practically immediate return on investment. Logically, it is much cheaper than manual extraction, but also than resorting to OCR or RPA, with the added advantage that it can treat unstructured data, which after all is almost 80% of what a company handles. business.
Thanks to the state-of-the-art technology that Docbyte works with, it has become an exceptional partner in helping organizations capture, classify, and consolidate information, regardless of the physical or digital channel through which it is received and the format in which it is received. That is present.
Through its solutions and with the help of Artificial Intelligence, this information is integrated into the company’s automated systems and processes to take advantage of all its wealth. Companies in sectors for which information is as critical as Banking, Health, Public Administration, or Utilities, among others, have already benefited from Docbyte’s services.