Intelligent Document Processing (IDP) refers to the automation of data extraction from unstructured documents. It eliminates the need for manual data entry, reduces errors, and increases efficiency in document management.
As we enter the sixth decade of the information age, data has become a currency of the business world. However, it is estimated that a vast majority of a company’s data remains unstructured, taking the form of written text found in various forms such as reports, contracts, and emails.
The manual process of collating this information requires a significant amount of time and resources, ultimately underutilizing or burdening a company’s most valuable asset – its human talent.
IDP is increasingly being used in various industries such as finance, healthcare, and government, to automate many document-intensive tasks such as invoice processing, contract management, and compliance reporting among others.
IDP is also being used to extract insights from unstructured data in many documents, thereby adding to the strategic value of business operations.
According to Straits Research, the worldwide market for Intelligent Document Processing (IDP) was valued at USD 1,035.81 million in 2021 and is expected to reach USD 6,382.52 million by 2027, with a compound annual growth rate (CAGR) of 35.4% from 2021 to 2027. During this projected period, the North American region is expected to hold the largest market share globally.
What is intelligent document processing?
IDP or intelligent document processing is a software that automates the process of extracting, processing, and analyzing crucial document data for business operations. Such solutions are key to modernizing and streamlining the often outdated, manual, and slow processes associated with document processing.
The “intelligence” in IDP comes from the use of Artificial Intelligence (AI) tools for data extraction. AI has the ability to process repetitive tasks without the cognitive limitations of humans; in fact, AI can produce more accurate results as it continues to process and learn.
How does Intelligent Document Processing work?
Intelligent Document Processing (IDP) typically involves a combination of optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) techniques to extract structured data from unstructured documents. Here’s a general overview of how IDP works:
- OCR is used to recognize and extract text from images or scanned documents, converting them into machine-readable text.
- The extracted text is then processed using NLP techniques to identify and extract relevant data such as names, addresses, dates, and numbers.
- Machine Learning algorithms are trained on a large dataset of labeled documents to recognize and extract specific information/fields from invoices, forms, or contracts.
- The structured data is then validated and cleaned, and any missing or incorrect data is corrected or flagged for manual review.
- The final output is a structured data format that can be easily integrated into other systems, such as databases or business intelligence tools, for further analysis and reporting.
IDP can learn and adapt to the specific requirements of different types of documents and industries, which makes it flexible and versatile. IDP also allows handling large volumes of unstructured data, making it an efficient solution for automating data-intensive tasks such as invoice processing, contract management, and compliance reporting.
What is the difference between IDP and automated document processing?
The importance of Intelligent Document Processing (IDP) and Automated Document Processing (ADP) cannot be overstated in today’s business environment.
As the volume, complexity, and speed of incoming data continue to increase, manual processing becomes increasingly inefficient and costly. Both IDP and ADP automate repetitive and time-consuming tasks associated with document processing, such as manual data entry, reducing errors, and increasing efficiency. They also provide better insights and decision making by extracting insights from unstructured data.
While IDP and ADP are both technologies that automate the process of extracting data from unstructured documents, there are some key differences between the two:
- IDP uses advanced technologies such as machine learning (ML) and natural language processing (NLP) to extract data from unstructured documents, whereas ADP relies primarily on optical character recognition (OCR) technology.
- IDP is more sophisticated in its ability to understand and extract complex data, such as text, numbers, and dates, and can also handle different types of documents and industries, while ADP is more focused on recognizing and extracting text from images or scanned documents.
- IDP can learn and adapt to the specific requirements of different types of documents and industries, and can produce more accurate results as it continues to process and learn, while ADP is more limited in its ability to adapt to different types of documents and industries.
- IDP is more flexible and versatile than ADP, allowing for the handling of large volume of unstructured data, making it an efficient solution for automating data-intensive tasks such as invoice processing, contract management, and compliance reporting. ADP is more focused on the recognition and extraction of text from images or scanned documents.
Benefits of intelligent document processing
IDP is a powerful technology that can help businesses to streamline their operations through better management of data, improve their bottom line, and better compete in today’s fast-paced and data-driven business environment. The benefits of IDP are numerous and far-reaching, and businesses of all types and sizes are quickly realizing the value of this technology in streamlining their operations and improving their bottom line.
Here are some of the key benefits of IDP:
- Increased Efficiency: IDP eliminates the need for manual data entry, which can be slow, costly, and prone to errors. By automating the process of extracting data from unstructured documents, IDP can significantly increase the efficiency of business operations. This can lead to cost savings, as well as faster processing times, which can be especially beneficial for businesses that deal with high volumes of unstructured data.
- Improved Accuracy: A 2008 research paper analyzed the occurrence of human errors in data processing. According to this research, the probability of human error when manually entering data into simple spreadsheets is between 18% and 40%. In complex spreadsheets, that probability increases to 100%. The error does not arise because people are incompetent but because even the competent data processor is human, which is a prerequisite to “err” as the saying goes. IDP solutions are at least 95% accurate, and can eliminate costly and serious errors associated with manual document processing.
- Cost Savings: By automating repetitive and time-consuming tasks, IDP can significantly reduce labor costs. Additionally, IDP can help to reduce costs associated with errors and inaccuracies that can occur with manual data entry.
- Data security: IDP can extract data in a secure and compliant manner, adhering to regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This ensures that sensitive information is protected and that businesses are in compliance with relevant regulations.
- Better Decision Making: IDP allows for the easy extraction of insights from unstructured data, making the process of decision making easier and more accurate. This can be especially beneficial for businesses that need to make data-driven decisions, such as finance, healthcare, and government.
- Scalability: IDP can handle large volumes of unstructured data, which is a growing concern for many businesses. The technology can scale to meet the needs of organizations of all sizes, making it an efficient solution for automating data-intensive tasks such as invoice processing, contract management, and compliance reporting.
- Adaptability: IDP can learn and adapt to the specific requirements of different types of documents and industries, making it a versatile and flexible solution for businesses. This can help to ensure that the technology is able to effectively extract data from a wide variety of unstructured documents.
- Integration: IDP can easily integrate with other systems, such as databases or business intelligence tools, for further analysis and reporting. This allows businesses to easily access and use the data that has been extracted, without having to manually enter it into another system.
IDP can improve both employee and customer experience by eliminating the need for manual corrections, leading to faster approvals and reducing processing times. It also increases operational scalability by allowing valuable human resources to focus on more cognitive tasks instead of manual corrections. With >95% document accuracy, businesses can onboard more clients without increasing headcount or spending on training and retraining. The solution also ensures reliability by producing clean, error-free data that teams can rely on. Additionally, IDP drives down processing costs per document by eliminating the cost of manual data entry and only requiring the cost of the IDP solution, allowing for optimization of resource costs.
Use cases for intelligent document processing
The use of IDP is not limited to a specific sector, it can be applied to various industries, and it can be used to automate many different types of tasks. By automating data extraction tasks, processes can be expedited, both on an objective task-performance time scale as well as through the avoidance of errors that are commonly associated with manual data extraction processes. For example, without an IDP system, the manual extraction of 10,000 data points per day would take 10,000 minutes. However, with an IDP system with even 95% accuracy, only 500 values would need to be corrected, reducing the total time to 5,000 minutes. This results in a 50% reduction in effort.
- Invoice processing: IDP can be used to automate the process of extracting data from invoices, such as vendor information, purchase order numbers, and line item details. This can help to improve the efficiency and accuracy of accounting and finance operations, and also helps to streamline the Accounts Payable process.
- Contract management: IDP can be used to extract data from legal contracts, such as terms and conditions, expiration dates, and key performance indicators. This can help to automate the process of contract management and can also help to reduce the risk of errors and non-compliance.
- Compliance reporting: IDP can be used to extract data from regulatory documents, such as tax returns, safety reports and other compliance related documents. This can help to automate the process of compliance reporting and can also help to reduce the risk of errors and non-compliance.
- Resume/CV processing: IDP can be used to extract data from resumes and CVs, such as candidate contact information, education and work experience, skills and qualifications. This can help to automate the process of resume screening and candidate selection, speeding up the recruitment process.
- Legal document analysis: IDP can be used to extract data from legal documents such as contracts, agreements, and court orders. This can help to automate the process of extracting data from unstructured legal manuscripts that are filled with jargons.
- Insurance Claim Processing: IDP can be used to extract data from insurance claims, such as policyholder information, claim details, and medical documents. This can help to automate the claims processing and also to reduce the risk of errors and inaccuracies.
- Supply Chain Management: IDP can be used to extract data from shipping documents, purchase orders and other supply chain related documents. This can help to improve the efficiency of logistics operations and also to reduce the risk of errors and inaccuracies.
- Human resource management: IDP can be used to extract data from employee documents such as resumes, contracts and performance evaluations. This can help to automate the process of employee management and also to reduce the risk of errors and inaccuracies.
- Financial analysis: IDP can be used to extract data from financial documents such as bank statements, credit reports and loan applications. This can help to automate the process of financial analysis and also to reduce the risk of errors and inaccuracies.
- Research and Development: IDP can be used to extract data from scientific papers, research articles and technical documents. This can help to automate the process of research and also to reduce the risk of errors and inaccuracies.
The above applications of IDP make it a versatile and valuable technology for businesses of all types and sizes. Here are some examples of how different sectors would benefit from IDP:
- Finance: IDP can automate the process of extracting data from financial documents, such as invoices, bank statements, and contracts. This can help to improve the accuracy and efficiency of accounting and finance operations, and can help to reduce the risk of errors and fraud.
- Healthcare: IDP can be used to extract data from medical records and other healthcare documents, such as insurance claims, lab results, and patient information. This can help to improve the quality of care and can also help to reduce costs by automating the process of extracting data from unstructured documents.
- Government: IDP can be used to extract data from government documents, such as tax returns, licenses, and permits. This can help to improve the efficiency of government operations, and can also help to reduce the risk of errors and fraud.
- Retail and logistics: IDP can be used to automate the process of extracting data from invoices and other documents associated with retail and logistics operations. This can help to improve the efficiency of inventory management, and can also help to reduce costs associated with errors and inaccuracies.
- Legal: IDP can be used to extract data from legal documents such as contracts, agreements, and court orders. This can help to automate the process of extracting data from unstructured documents, and can also help to reduce costs associated with errors and inaccuracies.
- Real estate: IDP can be used to extract data from real estate documents such as property titles, purchase agreements, and lease agreements. This can help to automate the process of extracting data from unstructured documents, and can also help to reduce costs associated with errors and inaccuracies.
- R&D: IDP can be used to extract data from scientific papers, research articles, and technical documents. This can help to automate the process of research by making it easier to gather and analyze large amounts of information. For example, IDP can be used to extract data from articles and papers in a specific field of study, making it easier to identify trends, patterns, and connections. IDP can also be used to extract data from academic journals, enabling researchers to quickly and easily identify relevant literature in their field.
- Academia: IDP can be used to extract data from student resumes and application forms, making it easier to process applications and identify potential candidates. IDP can also be used to extract data from student records, such as grades and attendance, making it easier to track student progress and identify areas where students may need additional support.
How to choose the right IDP solution for your business?
When choosing an IDP solution, it is important to consider several factors to ensure that the solution meets the specific needs of your organization. First, accuracy is a crucial factor to consider. Look for a solution that has a high level of accuracy, ideally above 95%.
Next, consider scalability, and ensure that the solution can handle the volume of data that your organization processes. It is also important to look for a solution that can easily integrate with your existing systems and workflow.
Flexibility is also a key factor, as the solution should be able to be customized to meet the specific needs of your organization. Furthermore, it is important to ensure that the vendor provides good support, including regular updates and maintenance.
Security must not be overlooked in the choice of an IDP solution; you should look for a solution that follows industry standards for security and data protection. Finally, compare the pricing of different solutions to ensure that you get the best value for your money. It is also important to look for a solution that has been successfully implemented in similar use-cases to your industry or business.
When evaluating an IDP solution, it’s important to consider the following four core needs:
Core need 1: The core capabilities of the solution, including the ability to flag unreadable documents or fields for human intervention, reducing the workload for the human quality check process.
Core need 2: The vendor’s technology ecosystem, which should allow for realizing the full potential of automation and support for future updates and fault mitigation.
Core need 3: After-sale support and technological knowledge transfer from the vendor, which is important as the IDP market is still in its early stages.
Core need 4: The price and cost reduction potential of the solution, as different pricing models are available such as fixed price, pricing based on volume and complexity, and pricing based on the accuracy of output.
Nanonets is an intelligent document processing tool that uses machine learning to automate the process of extracting data from documents. It utilizes a combination of Optical Character Recognition (OCR) and deep learning algorithms to accurately extract data from various types of documents, such as invoices, receipts, and contracts. The user can train the Nanonets model by providing it with sample documents and the corresponding data that should be extracted from them. Once the model is trained, it can then be used to automatically extract data from new documents with high accuracy. Additionally, Nanonets also provides a user-friendly interface to help users to review and correct any errors in the extracted data.
- OCR: Nanonets uses advanced OCR technology to accurately recognize text, numbers, and other characters from documents, including handwriting and machine-printed text.
- Deep Learning: Nanonets utilizes deep learning algorithms to understand the context of the data and extract it accurately, even from complex and unstructured documents.
- Customizable: Nanonets allows users to train their own models by providing sample documents and the corresponding data that should be extracted from them. The user can also customize the model by adjusting the extraction rules.
- User-friendly Interface: Nanonets provides a user-friendly interface that allows users to easily review and correct any errors in the extracted data. Users can also export the extracted data in various formats, such as CSV, JSON, and Excel.
- Multi-Language Support: Nanonets supports multiple languages, which allows users to extract data from documents written in different languages.
- API Integration: Nanonets provides an API that allows users to integrate the IDP solution with other tools and systems, such as accounting software, enterprise resource planning (ERP) systems, and customer relationship management (CRM) platforms.
- Scalability: Nanonets is a scalable solution that can handle large volumes of documents and data, making it suitable for businesses of all sizes.
Nanonets offers several advantages as an IDP solution, such as its ability to handle a wide range of document types, its high level of accuracy, and its ease of use. With Nanonets, users can quickly and easily extract data from documents, which can save them a significant amount of time and effort.
AI is revolutionizing the way businesses operate, just as steam power did to industries in the 18th century. Businesses that can effectively utilize cutting-edge technologies like IDP will have significant advantages in terms of efficiency and effectiveness. These technologies have the power to automate processes, reducing errors and increasing efficiency. It’s important to keep in mind that AI-based automation platforms are not magic solutions, they are the outcome of careful planning and collaboration between experts to solve real-world problems.
With the growing demand for automation and the increasing importance of data, IDP is poised to play a vital role in shaping the future of business. The time to invest in IDP is now, for those who do will be the ones who reap the benefits in the long run.