AI, Chatbots, Large Language Model

Putting Generative AI to work for More Automated Documentation Processing

By 6 Minute Read


Why Documentation is a Significant Challenge for Businesses

Several industries rely heavily on documents, where the collection, data extraction, and processing of large volumes of varying documentation types are critical to their operations. At the same time documentation requirements can be burdensome and a significant cost driver.

This blog explores the impact of Generative AI in reducing costs, increasing automation, and improving the customer experience for such document-dependent businesses.

Financial service institutions, for example, are notable for their tedious documentation requirements and associated compliance rules and complexities. Customers and other stakeholders are faced with a myriad of forms and paperwork when opening accounts, applying for mortgages and loans, getting credit cards, and managing their loans and investments over their lifetime. Required documentation such as proof of identity, credit reports, income statements, insurance forms, and contracts need to be gathered, classified, validated, and processed in order to approve applications from financial customers. This makes documentation a crucial part of revenue generation for financial institutions. 

Documents and forms come in different formats, and can be submitted by users via mail or fax, or as digital image uploads, web forms, or email attachments. Their quality and accuracy can be as varied as their complexity. Multiply this by the sheer volume of different docs that may need to be gathered and associated with a single user and it’s easy to appreciate how messy and time consuming this process can be. 

Another challenge of handling multiple documentation variants is in correctly classifying them. In the maze of these documents also lies an abundance of unstructured content that needs to be extracted. The volume of different forms of documentation combined with their propensity to be lengthy and complicated make the extraction and validation of relevant data points an onerous, labor-intensive and costly task. 

Besides financial services, other industries that grapple with documentation processing costs and complexity include government services, healthcare, education, legal services, and real estate. While document types, compliance regulations, and data needs differ across the various industry sectors, the challenges around documentation are common to all. The traditional manual processing of documents in these industries is labor-intensive due to the need for high accuracy, compliance and privacy considerations, and the complex nature of the documents. 

The Role of Generative AI in Documentation Automation 

Document-intensive industries have embraced technologies to help better automate aspects of documentation gathering and processing. Optical Character Recognition (OCR), for example, converts an image of a document to a text format, which helps in automating the data entry process. This decreases the likelihood of errors associated with human data entry as well as reducing the associated costs. However, OCR faces limitations when document images are of poor quality and faces difficulty in handling diverse fonts and languages, special characters, and formatting issues. 

Other technologies like robotic process automation (RPA), digital forms and signatures, document management systems, and workflow automation tools have been implemented in order to drive down costs and improve efficiency and accuracy. Now, advancements in the ability of Artificial Intelligence (AI) to read and understand natural textual language via Large Language Models (LLMs) with greater precision and speed, enables businesses to quickly and easily parse through large amounts of data (including semi-structured and unstructured data) and convert it into structured formats. This has the potential to substantially impact how businesses automate the complete documentation handling process, by:

  1. Automating Document Collection: A conversational AI Assistant or chatbot, powered by conversational and generative AI, can interact with customers, via text or voice, to request and collect necessary documents, guiding users through an application or submission process. It can answer customer queries, clarify complex terms, and/or navigate them through a digital form (e.g. via web or mobile app) throughout the conversation. This makes the document gathering process more seamless and efficient for both the customer and the business. It also reduces the need for human agents to handle routine document-related queries so that they can focus on more complex customer queries and  issues. This has a natural upside for cost effectiveness.
  2. Validating Documents.  AI systems can be trained to check documents for completeness, accuracy, and authenticity. Advanced AI models can compare submitted documents against databases and predefined rules or compliance requirements to validate key data and information. Applying AI drastically reduces the time spent on document verification, which can be a very labor-intensive task. This leads to significant time and cost savings.
  3. Chasing Missing Documents.  After identifying invalid or missing documentation associated with a user’s application status or a customer record, an AI Assistant can then proactively reach out to them to request the missing documents and capture them as images.
  4. Processing and Analyzing Data in Documentation. Generative AI can help in extracting and processing data from documents, converting unstructured data into structured formats that are easier to analyze and use. Techniques like natural language processing (NLP) and optical character recognition (OCR) are employed to read text and interpret content.
  5. Compliance and Error Checking of Documentation. Generative AI can ensure compliance with relevant regulations and standards, for example anti-money laundering (AML) and Know Your Customer (KYC). It can automatically check for inconsistencies or errors in the application, flag potential issues, and even suggest corrections, thus maintaining high standards of compliance and reducing legal risks.
  6. Data-Driven Decision Making and Approvals. AI can assist in preliminary decision-making by processing data extracted from customer documentation to ensure that it meets predefined criteria, such as risk assessment and eligibility criteria. Such data can be a powerful tool in the approval processes. For example, using AI, a mortgage company may be better able to tailor a mortgage to a prospective loan customer based on key data extracted from the documents gathered in the application process.  This helps reduce risk and offer more personalized recommendations to loan customers. This ultimately can help improve the financial health of a lender’s mortgage portfolio.

    Applying generative and conversational AI in document processing addresses some of the challenges of manual operations by increasing efficiency, reducing errors, and speeding up the overall application to approval processes. By leveraging AI, industries can shift from tedious manual document handling to more strategic and customer-focused tasks, enhancing productivity and service quality. This shift not only reduces operational costs but also improves compliance and accuracy, significantly transforming document-intensive workflows.

    Scenario: Leveraging AI in Mortgage Refinancing

    To illustrate the impact of AI on a document-intensive business process, we can explore the mortgage industry. It is not only the loan origination process that is document-heavy as a customer applies for a mortgage. There are also aspects of mortgage servicing that require significant document gathering and processing, such as a loan modification process for customers experiencing financial hardships and mortgage refinancing for customers looking to take advantage of lower interest rates or change their payment terms. Taking the scenario of mortgage refinancing, we can illustrate the powerful impact of generative and conversational AI.

    Mortgage refinancing requires the assessment of a homeowner’s current financial status, property value, and the potential benefits of a new loan structure. This naturally requires engaging with the lender and providing the essential information and documentation so that a loan can be customized and approved for a customer. 

    Engagement and Information Gathering
    A conversational AI assistant on the lender’s website or mobile app can ask preliminary questions to understand the homeowner’s goals for refinancing, such as lowering monthly payments, cashing out equity, or changing loan terms. It can collect necessary personal and financial information, guiding the homeowner through the process of submitting or updating details about their income, current mortgage, property information, and any significant financial changes since their last loan application.

    Document Submission and Verification

    The AI assistant requests the documents required for the refinancing application, such as recent pay stubs, tax returns, and a statement of the current mortgage. It can direct the homeowner to upload these documents directly through the interface. Using OCR and machine learning, the AI processes these documents to verify accuracy and completeness. It automatically fills in details on the application form, reducing manual entry and the potential for errors.

    Loan Options and Customization

    With the data provided, the AI analyzes current loan rates, homeowner’s equity, and financial data to suggest the most beneficial refinancing options. The AI can simulate different scenarios showing how adjustments in loan terms or rates could affect monthly payments and overall interest paid. The homeowner can interact with the AI assistant to modify assumptions or explore various “what-if” scenarios, receiving instant recalculations and advice tailored to their specific situation.

    Approval Processes and E-Signature

    Once the homeowner selects a refinancing plan, the AI prepares the necessary application forms and contracts. It sends these documents to the homeowner for review and uses an integrated e-signature tool to gather signatures electronically. This is then submitted to human underwriters for final review and approval, notifying the homeowner of the progress and any additional requirements via automated messages.

    Closing and Follow-up

    After approval, the AI schedules a closing date and coordinates with all parties involved, including real estate agents, lawyers, and title companies. It provides a checklist of what the homeowner needs to prepare for the closing day. Post-refinancing, the AI assistant remains available to answer any questions about the new mortgage.

    Using generative and conversational AI in this way enhances the customer experience by providing a fast, personalized, and transparent refinancing process. It reduces the processing time and overhead costs for the lender while ensuring accuracy and compliance. 

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