Business documents are often available in PDF, scanned copies that exists outside of enterprise systems. Traditionally, workers are required to manually extract relevant data from these documents and input it into their company’s ERP or accounting software, which is a time-consuming and costly process. Additionally, this type of data entry work can be tedious and prone to high levels of human error, further adding to the inefficiency of the process.
What is document processing?
Document processing is a technique to process all kinds of business documents that are available outside of enterprise systems in the form of pdFs, scanned copies, images etc.
Fortunately, in recent years, the emergence of Document AI or Intelligent Document Processing (IDP) has revolutionized the field of business document processing by leveraging the power of AI and machine learning. This technology has enabled organizations to efficiently and accurately process a vast array of documents, including those that were previously considered challenging to manage. With IDP, businesses can now streamline their document processing workflows, reduce costs, and minimize human error, ultimately improving overall efficiency and productivity.
How to automate document processing workflow?
A document processing workflow typically involves five major steps, and end-to-end automation may require the use of multiple technologies and tools. The steps are outlined below:
Documents are everywhere and they can be sourced from various locations, including emails, file servers, cloud storage services, and more. To achieve end-to-end automation, documents must be automatically collected from these sources and sent to the AI engine for data extraction. This can be accomplished by using a variety of methods, such as API integration, email reading and forwarding, or integrations with cloud storage services like Dropbox, Google Drive, or OneDrive.
Integration with these sources can be achieved through several tools and technologies, such as Robotic Process Automation (RPA) or integration platforms that provide pre-built connectors and adapters to interface with various systems. By automating document collection, businesses can improve their processing time, reduce errors, and increase overall efficiency.
Extracting data from documents:
This is the most critical part of document processing automation. Depending on the complexity, volume, and diversity of the documents, different tools and technologies should be selected for optimal results. Document AI or IDP is one of the most advanced and powerful technologies for document data extraction. It goes beyond traditional OCR and uses techniques like Natural Language Processing (NLP) and machine learning to understand the context of documents and read them in a way similar to humans. This allows IDP to extract data from complex, unstructured documents with high accuracy.
For certain documents, pre-processing and post-processing may be necessary to achieve the desired level of accuracy. Pre-processing can involve tasks like image enhancement, noise reduction, and image binarization to prepare the document for data extraction. Post-processing involves reviewing and validating extracted data to ensure its accuracy and completeness. Pre-processing and post-processing are handled programmatically.
Validating the captured information: Data accuracy is very important and captured data should be validated before processing them further. In some cases data validation can be handled programmatically however, you may need to have human in loop to validate the captured data or approval.
Applying business rule:
For certain business use cases, it may be necessary to apply specific business rules before the extracted data can be sent to existing enterprise systems. In such cases, the captured data may need to be processed programmatically or with automation tools such as RPA before being sent to the enterprise systems.
For instance, businesses may want to perform data cleansing or data transformation to ensure that the extracted data is complete, consistent and compatible with existing systems. These checks can be performed using custom scripts or automation tools that implement specific business rules and logic.
Populating data into existing systems:
Once the data has been captured, validated, and transformed, it is ready to be sent to the existing enterprise systems to complete the automation process. This integration can be achieved through various ways, such as API integrations, webhooks, RPA, or other integration tools.
API integrations allow for direct communication between the document processing system and the enterprise system, enabling real-time data transfer and reducing manual intervention. Webhooks are another method for integrating systems, allowing the document processing system to push data to the enterprise system when specific
events occur (example: when a document is processed by AI and ready to push) in real time.
RPA can be used to automate the transfer of data to enterprise systems. This method is particularly useful when the enterprise system lacks APIs or when APIs do not offer the necessary level of integration. Example of such system is Infor Visual, a desktop based ERP.
Overall, by leveraging the power of AI and machine learning, businesses can improve the accuracy and efficiency of their document processing workflows, while also reducing costs and freeing up employees to focus on higher-value tasks.