- Document AI uses machine learning and natural language processing to read printed or written text and extract information from it.
- A Document AI model is built in five steps: ingest, label, train, deploy, and consume.
- Labeling the initial data shows the model what to look for in the documents it analyzes.
- Document AI is used in legal review, insurance client onboarding, and commercial banking loan approval.
- Document AI can label and organize unstructured data so it is ready for later use.
What is Document AI?
Document artificial intelligence (AI) is a technology that uses machine learning (ML) and natural language processing (NLP) to read and understand printed or written text, mimicking a human. It can be trained to analyze an organization’s documents to find relevant information and data insights, and can categorize and label the documents to make them easier to utilize. Many businesses have years of data stored in documents such as financial statements, receipts, contracts, invoices, etc. Document AI technology helps organizations analyze and digitize these documents much faster than they could by hand, while reducing human error.
How Does Document AI Work?
Document AI goes through five steps in order to learn how to effectively analyze an organization’s specific documents:
- Ingest - The AI is provided with a wide variety of documents. Document AI automatically recognizes various data types and formatting within the documents.
- Label - The initial data provided to the model must be labeled in order to prepare the data for the training process. Labeling existing data shows the AI model what to look for in the documents it analyzes.
- Train - The AI learns from the document set and creates an ML model specialized to the desired tasks.
- Deploy - The newly created model must be deployed into a workable environment so it can be used with new input documents.
- Consume - The model is ready to be used for business applications. Users continue to provide oversight and make any necessary adjustments.
Once trained, a Document AI model will be able to read and recognize text and other data formats, extract desired data from new documents based on prepared input, and generate important insights based on the data extracted.
Use Cases for Document AI
Document AI is especially useful for tasks where a large number of documents containing potential insights must be analyzed quickly and efficiently. There are several industries in which this is useful:
Legal Documents - Lawyers are often responsible for interpreting many documents related to their cases and clients. Whether they are reviewing laws and regulations, preparing for a case, or going over contracts, documents and information are their bread and butter. Document AI helps legal teams digitize and sort through documents to find the most relevant information.
Insurance Agencies - When insurance companies take on new commercial clients, reams of data and documentation must be analyzed to fully understand a client’s needs and risks. Document AI improves this process by automating administrative tasks and providing important insights from the data provided.
Banking/Finance - In commercial banking it is often necessary to review a large amount of documentation in order to understand financial and legal risk. Document AI can process and analyze financial documents to aid in client onboarding and loan approval.
Benefits of Document AI
In the information age, accurate and complete data are the keys to business success. However, it is estimated that in most companies, 80% of available data is in an unstructured format such as free-form text or hard copy documents. Traditional document processing requires human analysis of each document in a time consuming process. Document AI technology can analyze, organize, and learn from these documents quickly, saving time, money, and manpower to be used in other areas. This leads to more productive and efficient workforces and helps businesses to make well informed business decisions. Document AI also reduces the opportunity for human error in document processing, creating accurate and consistent data. Document AI can also be used to label and organize unstructured data in order to prepare it for future use.
Document AI in H2O AI
H2O Document AI classifies documents and extracts text, tables, and fields from scans, faxes, PDFs, forms, and emails, delivered through Enterprise h2oGPTe. H2O Document AI.