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Businesses are overwhelmed by the increasing amounts of data and documents they must handle daily, as Fortune 500 companies lose an average of $12 billion a year due to a lack of document management.
However, one way to deal with this problem is to use intelligent document processing, which allows businesses to save time and money while gaining useful insights from large amounts of data.
What Is Intelligent Document Processing?
Intelligent document processing uses advanced artificial intelligence (AI) technologies to automate the extraction and interpretation of data from various document formats.
Some AI technologies used in intelligent document processing software include:
- Natural language processing (NLP) enables the system to understand and interpret human language within the documents. It analyzes the context and semantics of the text, which is essential for extracting specific information and understanding the document’s purpose.
- Optical character recognition (OCR) converts different types of documents, such as scanned papers and PDF files, into editable and searchable data. It makes data accessible for further processing.
- Machine learning (ML) algorithms help the intelligent document processing system improve its accuracy over time by learning from corrections and manual inputs provided by the users.
- Deep learning algorithms are useful for recognizing complex data patterns. This is important for understanding and categorizing the finer details within a document, which traditional algorithms can’t do.
- Computer vision uses deep learning techniques to interpret and analyze images within documents. More importantly, it detects and extracts text from images and unstructured formats like presentations and reports so businesses can accurately convert visual data into actionable insights.
The Role of Generative AI and LLMs in Document Processing
Generative AI and LLMs offer more advanced solutions than traditional methods, which struggle to keep up with the high volumes of data that businesses need to manage.
LLMs are equipped with NLP capabilities to accurately extract relevant information from various business documents, including those with complex structures and varying formats. This gives businesses peace of mind, knowing they can quickly retrieve and analyze critical information from documents like contracts and invoices without any manual intervention.
Unlike simpler AI models, LLMs can understand the context within the text. This lets the model gain a human-level understanding of the document, which is vital for tasks such as summarizing patient records, identifying key clauses in legal documents, or automating claims processing.
LLMs also drastically reduce the chance of human error due to their ability to learn from data and improve over time. As a result, document processing accuracy increases, so businesses can have inaccuracies that need corrections.
Conversely, generative AI helps with document generation by automatically drafting text based on the input data. This is especially useful for creating customized customer communications, reports, and summaries. It ensures consistency in tone and style while adapting the content to the organization's specific needs.
Another critical aspect of generative AI is its ability to simultaneously handle high amounts of documents.
Generative AI lets businesses process data quicker than a human team, resulting in increased operational efficiency, quicker turnaround times, and reduced costs. It’s also worth noting that generative AI lets businesses classify and extract data from documents without requiring any technical expertise.
How Does Intelligent Document Processing Work?
Document AI is an example of how AI-powered document processing works.
It involves a five-step process for turning unstructured data into a structured format that businesses can use to gain practical insights.
1: Document Pre-Processing
Document AI starts by handling the initial clean-up of the documents.
This includes:
- Correcting orientations
- Enhancing image quality for readability
- Identifying different sections of the document
As a result, technologies like OCR and NLP will perform better in the subsequent steps.
2: Classification
After pre-processing documents, Document AI classifies them based on pre-determined categories such as department, confidentiality level, or type (invoices or contracts, for example). This helps route the documents to the appropriate workflow and ensures data handling complies with policy standards.
3: Data Extraction
Document AI then applies OCR to convert text within the document into a machine-readable format. It uses NLP to understand the context and extract relevant data like:
- Dates
- Names
- Amounts
As a result, the raw data from the document is converted into practical insights that businesses can benefit from.
4: Data Validation
The next step involves verifying the accuracy of the extracted data. It cross-references the data against existing business databases or uses pre-defined rules to check for errors or inconsistencies. Data validation is key to ensuring that data entered into business systems is accurate and reliable.
5: Integration
The last step of the process involves integrating the processed data into the business’s main operational systems, such as customer relationship management (CRM) or human resources (HR) systems. Document AI eliminates manual intervention by letting businesses automatically transfer key information from their documents to their systems.
Integration speeds up business processes and significantly reduces the chances of human errors, which are common when performing manual data entry.
Benefits of Intelligent Document Processing
Some of the benefits that intelligent document processing provides for businesses include:
Enhanced Efficiency
intelligent document processing automates the extraction, processing, and classification of data from documents. This automation speeds up workflows that generally rely on manual input.
Some examples include entering data into enterprise resource planning (ERP) systems or updating customer records. Consequently, businesses free up employee time to focus on higher-value tasks like strategic planning and product development. This is especially relevant considering that professionals spend up to 50% of their time searching for information.
Improved Accuracy
intelligent document processing uses technologies like NLP, OCR, and ML to reduce the chance of errors. ML learns from previous data, making it more accurate over time.
It provides a massive advantage for businesses that deal with sensitive or important information, such as financial statements or personal data, by reducing the chance of costly mistakes.
Cost Reduction
intelligent document processing helps businesses drastically reduce operational costs by reducing labor costs associated with manual document sorting, data entry, and error correction. These savings can be directed toward more strategic investments, such as product innovation and employee development.
Scalability
As businesses grow, so does the volume of documents and data they have to deal with. intelligent document processing systems can easily scale to manage growing document loads without hiring additional employees. This means businesses can expand without dealing with proportional increases in overhead costs.
Improved Compliance
Regulations like GDPR require exceptional record-keeping and data accuracy for businesses to follow successfully. Intelligent document processing helps maintain compliance with various regulations, such as NAIC in the insurance industry, by processing and storing documents correctly.
Furthermore, features like audit trails and secure data handling also help meet stringent compliance requirements.
Better Customer Experience
Quick and accurate document processing leads to faster responses to customer queries and requests. Whether processing loan applications, insurance claims, or customer onboarding documents, intelligent document processing helps businesses serve customers more efficiently and boost customer satisfaction.
Data Accessibility
Intelligent document processing converts unstructured data into structured formats, making it easier for businesses to find and analyze key data. Consequently, businesses can get the most out of their data by making more informed decisions quickly. It also lets businesses stand out in the market by accessing advanced analytics from structured data.
Examples of Document Processing: Industry Use Cases
Document processing has practical applications across various industries.
However, we will focus on three primary industries: healthcare, banking, and insurance.
Healthcare
Intelligent document processing improves the management and processing of key documents like patient records by automating data extraction and document classification. This lets healthcare providers quickly access patient information whenever needed, resulting in better patient care.
For example, intelligent document processing can extract patient histories, treatment information, and medication details from various forms. This eliminates manual data entry and allows healthcare staff to make quicker decisions.
Less time spent on repetitive tasks, more time spent on patient care.
Banking
Intelligent document processing simplifies complex processes like:
- Loan applications
- Customer onboarding
- Compliance documentation
The AI system extracts key data points from financial documents so banks can quickly assess loan applications and manage risk more effectively. It also automates compliance processes, allowing banks to follow updated regulations without extensive manual intervention.
For example, intelligent document processing automatically classifies documents based on confidentiality levels and extracts vital information for Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, leading to faster processing times and improved accuracy.
Insurance
Insurance companies can use intelligent document processing to automate the handling of complex data and policy documents with high accuracy. It also makes it easier for businesses to manage policy details and risk assessments.
For instance, intelligent document processing lets insurance companies sort through policy details and analyze risk data quickly. It excels at extracting details from various policy documents, such as coverage terms and conditions, which means businesses can carry out risk assessments more accurately.
Additionally, intelligent document processing automates the extraction of critical data from insurance documents so businesses don’t need to waste time searching for key information. It also allows for the immediate retrieval of policy details and claim statuses, which helps employees provide informed responses to customer queries.
Automating Your Document Processing
Thinking about automating your document processing to reduce costs by 80% per processed document like we did for our clients? Schedule a free 30-minute consultation to see how Document AI works in a live demo and how we can implement it to best suit your organization’s needs.
FAQs
What separates intelligent document processing from traditional document management systems?
Intelligent document processing uses advanced AI technologies such as ML and NLP to automate the extraction, interpretation, and management of data from various document formats. Unlike traditional systems, these platforms can efficiently handle unstructured documents by adapting to multiple data types and layouts without human intervention.
Can intelligent document processing work with paper-based documents?
Yes, intelligent document processing uses OCR to digitize paper documents. This is important for businesses transitioning from traditional paper-based systems to digital workflows, as it ensures seamless integration of all historical and incoming data.
What impact does intelligent document processing have on customer experience?
Intelligent document processing solutions improve customer experience by speeding up response times, providing accurate data, and giving customers quick access to any information they need. This means businesses can use intelligent document processing to improve service delivery and customer satisfaction.
What are the key features of an intelligent document processing platform?
A robust intelligent document processing platform should have automated classification, data extraction, and validation capabilities. It integrates advanced technologies like computer vision and deep learning to handle structured and unstructured documents.
As a result, intelligent document processing platforms let businesses automate their data processing and make their data readily available.
