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How to Use AI Technologies to Enhance Corporate Document Processing

How to Use AI Technologies to Enhance Corporate Document Processing

Although today’s businesses exist in a digital age, document processing remains a challenge. Text-based data can be difficult to organize due to a diversity of formats. Fortunately, the development of artificial intelligence opens up new ways of simplifying document processing for business. Some of these practical solutions include recognizing and processing text, improving data quality, and aggregating data for analytics. Let’s dive in and learn more about what makes these approaches effective.

Components of AI Data Management System

The pipeline of AI document processing is as follows:

  1. Optical Character Recognition (OCR): OCR is used for recognizing text. OCR can function with multiple cameras, making it easy to scale up. However, challenges can arise if the input parameters vary too much.
  2. Natural Language Processing (NLP): Language generation and interpretation models can recognize the sentence and semantic structure of documents. NLP components can be highly complex, as they can perform many tasks such as:
    1. Document classification: the generative model can organize documents by layout, contained information, and domain.
    2. Data validation: when data is extracted, generative AI can determine if it matches the original source of the data via techniques like reference data check, pattern matching, and statistical and contextual analysis.
    3. Layout and entity recognition: the AI can determine what part of the data is most important, like the names of your clients, addresses, phone numbers, and insurance numbers.
  3. Data quality improvement: This process increases the accuracy, completeness, consistency, and reliability of extracted data. Unsupervised learning can handle some of these tasks like standardization and deduplication. However, error handling and policy compliance should be handled manually.
  4. Analytics: Getting insights on the extracted data can be done with a variety of interfaces. These systems can be very large in their own right due to the size of analytics models like demand or sales forecasting, other predictive models, and visualization graphs.

The Advantages of AI Document Processing

Although larger organizations like banks, insurance companies, and hospitals are most likely to feel the burden of document processing, these issues can affect smaller businesses as well. Making document processing more efficient with AI technologies has become far more accessible to mid-sized and small businesses in recent years. This can allow them to:

  • Reduce their administrative burden
  • Streamline their business processes
  • Organize their document repositories
  • Reduce risks and costs of human error in document processing

Since small businesses lack the extensive resources of their larger counterparts, making document processing more efficient can be essential to their success.

What Types of Documents Can Be Processed with AI?

OCR and natural language processing when paired together can achieve a number of complex tasks, including classifying a variety of documents and understanding their content. This strategy is most effective when used with training data. Fortunately, many of these documents are produced as a result of the business’s operations. Each passing day, new documents are created and edited that can be used as training data for the algorithm.

Here are some of the different types of documents that an algorithm can be trained to recognize:

  • Invoices
  • Purchase orders & receipts
  • Contracts
  • Packing slips
  • Tax forms
  • Legal documents
  • Healthcare records
  • Insurance documents
  • Real estate documents
  • Utility bills
  • Identity documents

Unique Document Types

Processing certain document categories can be more challenging than others. For example, manufacturing and engineering blueprints require a unique approach to processing. Few out-of-box models exist that can process these documents, and even they have limitations. A custom solution is typically needed to recognize and process these blueprints effectively. Typically this will require AI application development experts who have previous experience with this type of tasks.

Choosing a Document Processing Solution

There are many document processing solutions available to different business categories. Consider your business’s needs first and foremost. For example, a SaaS vendor who provides document processing for other businesses will have different needs than a business looking to process documents in-house.

Generally speaking, the more unique documents you have, the more difficult it will be to find an out-of-box solution. This means you’ll be more likely to succeed with custom software. However, if you deal with more regularly structured documents, you may just need to finetune some existing solutions.

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