Does Intelligent Document Processing (IDP) Use Artificial Intelligence

Intelligent Document Processing (IDP) automates manual data entry from digital copies of paper documents. It can take data from unstructured documents necessary for everyday business functions. IDP speeds up data entry in finance, human resources, insurance, the medical industry, logistics, and many other domains.

The “Intelligent” component in IDP comes from the use of artificial intelligence (AI). In IDP, AI algorithms use Computer Vision, Natural Language Processing (NLP), and Optical Character Recognition (OCR).  The algorithms classify, categorize, and extract data from various types of documents.

In this blog, we explain intelligent document processing and artificial intelligence. And explore their components, features, benefits, and applications. 

Understanding Intelligent Document Processing using Artificial Intelligence

Intelligent document processing relies on OCR, NLP, and Computer Vision to classify documents, extract and validate data, and finally integrate quality data into the business process. A typical workflow using IDP is explained in the following diagram by Gartner:

The different phases of intelligent document processing with artificial intelligence are as follows: 

1. Data Ingestion: Data is collected from various sources and formats, including JPEG, PDF, PNG, TIFF, and more, before preparation for processing. This preparation involves merging/splitting documents and correcting low-quality renders. Some IDP solutions also offer tools for data labeling and annotation.
2. Document Classification: Next, AI classifies documents into different categories. These include statements, invoices, purchase orders, legal contracts, etc. The AI categorizes documents based on predefined rules to meet the process needs. 
3. Data Extraction: Machine learning algorithms extract necessary data from classified documents and images. Relevant data fields get picked from the classified documents using OCR and CV. These fields include dates, amounts, names, addresses, and reference numbers.
4. Validation and feedback loop: The extracted data undergoes validation against internal and external datasets. At this step, human input is used to address outliers, preprocess data, classification improvements, and extraction quality assessment. The feedback loop is also introduced for error correction and training machine learning models. 
5. Data Integration: Validated data is then forwarded to the business process applications. IDP solutions integrate with various systems, including customer service platforms, data enrichment tools, and Robotic Process Automation (RPA) solutions. The integration is necessary to have the processed data available for business activities and insights for decision-making.

How does Intelligent Document Processing (IDP) use Artificial Intelligence (AI)? 

OCR and other document-processing software have been in use for the last two decades. However, they have limited capabilities compared to Intelligent Document Processing. OCR’s role in the manual process was to scan documents and make digital copies. IDP uses AI to remove most manual processes and take over classifying, extracting, and understanding data. 

Role of GenAI in Intelligent Document Processing

Generative AI, or GenAI, offers a significant advancement in Intelligent Document Processing. Unlike traditional AI, GenAI possesses greater autonomy. It is capable of generating content and insights based on algorithms. GenAI's ability to learn and improve through a feedback loop sets it apart. It can generate content based on the patterns and examples found within the data.

The approach allows GenAI to mimic human-like intelligence and creativity. Hence, it’s effective in document summarization, content generation, and even data synthesis tasks. In IDP, GenAI enhances data extraction by interpreting information from various document types. 

How AI Enhances Intelligent Document Processing Capabilities

GenAI enhances document processing through advanced classification, data extraction, interpretation, and analysis capabilities. It can interpret information from various documents with structured, semi-structured, and unstructured data. 

GenAI can analyze patterns and examples within the given data. Feedback loops improve its ability to extract key data points. These include dates, amounts, names, and addresses. This autonomous learning capability allows GenAI to adapt to different document formats. It is also capable of extracting data from documents that contain improperly printed, obscured information and even filter out duplicate information.  

Integration of AI Technologies in Intelligent Document Processing Solutions

Computer vision enables IDP systems to interpret visual information from documents, including scanned images, forms, tables, charts, and different sections. This allows accurate data extraction. 

NLP enables the IDP system to understand and extract information from unstructured text. This could include a wide variety of document types used in businesses, such as contracts or emails, which have to be analyzed for language patterns and context. 

Machine learning algorithms are trained to recognize patterns within documents. Continuous training and feedback can improve data extraction accuracy over time. The AI technologies work together seamlessly to automate document processing workflows.

Challenges in Intelligent Document Processing and How GenAI Solves Them

There are many challenges encountered by intelligent document processing with artificial intelligence. Here are some common challenges and their solutions: 

1. Data Collection and Analysis

Challenge: Gathering and analyzing large volumes of data from various document types.

Solution: GenAI is like a super-powered organizer for all sorts of documents. It can pull out the important info, understand it, and even learn from mistakes to get even better! This makes analyzing data way easier and more accurate.

2. Protection and Security

Challenge: Ensuring the security and privacy of sensitive information contained within documents. 

Solution: GenAI employs advanced encryption and authentication techniques to protect sensitive data. It also has strict access controls to limit access to sensitive information. It also ensures compliance with data protection regulations and safeguards against unauthorized access.

3. Legal and Documentation Issues

Challenge: Addressing legal concerns surrounding the use of GenAI for document processing. Also ensuring compliance with legal and regulatory requirements must be ensured. 

Solution: Gen AI makes sure everything is accurate and follows the rules, just like doctors and hospitals have to! It also ensures complete transparency in processes within organizations.

4. Integration and Reintegration

Challenge: Integrating Intelligent Document Processing (IDP) solutions with existing business software and processes

Solution: GenAI facilitates seamless integration by providing flexible APIs and connectors. This capability enables IDP solutions to interact smoothly with existing systems and workflows. 

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Real-world examples of AI-powered Intelligent Document Processing applications

Intelligent document processing with artificial intelligence transformed document management across various sectors. Because it helps streamline routines and get things done faster, this method is replacing manual work in almost every field. Here are some real-world examples showcasing the practical impact of AI in document processing:

1. Healthcare records management

IDP solutions with AI can help manage health records better.  These programs make things faster and more accurate, especially when dealing with documents like bills, insurance papers, and all those other documents. 

Regulatory compliance is another critical aspect where document processing solutions shine. Regulation compliance automation frees up physicians' and nurses' time for paperwork so they can dedicate more time and attention to patient care.

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2. Finance departments

In finance departments across organizations, AI-powered IDP solutions for finance automate the processing of invoices, receipts, and financial statements. In most sectors, the finance process requires careful human oversight to reduce errors. With IDP solutions, financial data processing is accurate with reduced processing times. For instance, IDP solutions extract and categorize data from invoices. This reduces errors and speeds up payment cycles. The result is on-time payment for vendors and employees without payment mistakes. 

IDP has a significant impact on the banking process. Banks deal with a mountain of paperwork, including KYC documents, loan applications, and credit/debit card applications. IDP has cut down delay times and made processes fast. 

3. Human resources

In Human Resources, managing large volumes of resumes, employee records, and onboarding documents is time-consuming. IDP solutions offer a shortcut to these challenges by automating document classification, data extraction, and compliance management, and even assisting with accounting. 

IDP streamlines document processing workflows, improving efficiency and reducing the administrative burden on HR professionals. With the administrative tasks automated, HR professionals can focus on strategic initiatives like talent management. 

4. Government agencies

Government agencies deal with large volumes of paperwork daily. Manual processing, as everyone has experienced, can take time and effort. It leads to delays, errors, and compliance issues. Manual handling of documents such as permit applications, license renewals, and public records often leads to delays and backlogs. 

AI-powered IDP solutions are ideal for handling government agencies' heavy workflows. By leveraging AI algorithms, they can extract data from various document types. They can also speed up the process by classifying documents and routing them to the appropriate departments, reducing processing times and improving overall efficiency.

5. Insurance companies

In the insurance industry, manual document processing for agent claims is the norm. Data entry, document classification, and claims processing could be faster and more manageable for customers. IDP solutions can boost claims' speed by extracting relevant data from different document types faster than human agents. With IDP, insurance companies can gain a competitive edge and deliver better customer service. 

6. Law firms

The legal sector deals with extensive documentation and processing. It often involves documents with handwritten content, pictures, and numbers. Law professionals also spend more time on contract analysis and document review. IDP can reduce the workload of lawyers by automating document analysis and review. For example, the software can sort and extract relevant information from legal documents, contracts, and case files that the lawyer seeks. The algorithms can also identify important case insights and speed up the process. 

7. Logistics companies

The logistics sector also relies on heavy paperwork for business processes. It’s integral to shipping orders, invoices, clearance documents, reports, and delivery receipts. Manual data entry and document processing are cumbersome. It causes delays, inaccuracies in data entry, and operational inefficiencies in the industry. 

IDP solutions integrate with logistics solutions to speed up document processing. It extracts and categorizing data from shipping documents, invoices, and delivery receipts. The solution saves all the wasted time on manual processing.  

How can Docsumo help with Intelligent Document Processing?

Docsumo is one of the leading service providers of intelligent document processing with artificial intelligence. The platform seamlessly integrates with various business processes and document workflows. Docsumo’s trained APIs, which require minimal integration time, are what set us apart. They save our clients valuable resources that would otherwise go into building models from scratch.

Docsumo's proprietary AI technology and machine learning algorithms automate data workflows with minimal operator intervention. Our advanced technology ensures accurate data extraction and processing, allowing your businesses to streamline their document processing workflows effectively. 

Docsumo is adept at processing a wide range of documents. It can process bank statements, purchase orders, certificates, invoices, and agreements. It operates in four simple steps:

1. Document Classification & Split: The platform ingests documents from various sources and classifies them.
2. Data Extraction: Ready-to-use AI/trained AI models extract data from the documents.
3. Human Validation: A human-in-the-loop reviews and validates the data quickly to ensure accuracy.
4. Integration: The validated data is integrated with organization-specific software such as CRMs, ERPs, and other systems.

Docsumo integrates with various business processes and document workflows. Its versatility makes it a preferred solution by leaders in sectors such as finance, insurance, real estate, healthcare and logistics.

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Conclusion

Artificial Intelligence is an integral part of Intelligent Document Processing (IDP). The integration of advanced NLP, cloud-based solutions, and blockchain promises a promising future for IDP. 

Advanced NLP will enhance the accuracy of data extraction from unstructured documents. This will benefit industries with handwritten and difficult-to-read content. Cloud-based solutions will improve scalability and accessibility, making them available to all companies. Blockchain integration will ensure enhanced data security and integrity, providing users with a safer and more reliable document processing experience.