When It Comes to Processing Insurance Documents, It’s AI to the Rescue

Vast document loads are pushing insurers to upgrade manual processes for more efficiency and accuracy.

Insurance companies deal with an overwhelming volume of policy documents, claims forms, invoices, underwriting records, and other paperwork. They face formidable challenges managing, processing, and extracting insights from the wealth of information available to them. Conventional approaches to document handling have been characterized by manual data entry, sorting, and analysis. These factors incur significant costs and introduce a considerable margin for human error, leading to inefficiencies, delays, and compromised customer experiences.

Despite significant investments in digitization, unstructured documents are a key part of an insurer’s operational processes. Repetitive, manual tasks such as data entry are perfect candidates for increasing operational efficiency by introducing artificial intelligence (AI).

Applications of AI in the insurance space are continually evolving with the release of new technologies, new solutions, and increased efficiency. When it comes to processing physical documents, this has led to an evolution from optical character recognition-based text ingestion to broader processing of unstructured data. Intelligent document processing (IDP) now covers the ingestion of a broad range of structured, unstructured, and semi-structured documents. IDP solutions have evolved into three broad categories: general-purpose solutions, insurance-vertical solutions, and fit-for-purpose solutions.

Often offered by big-tech companies such as Microsoft, AWS, and Google, general-purpose solutions are the most broadly applicable. They are trained to process documents of any sort from any industry. General-purpose solutions will be widely applicable across the enterprise but will take significantly longer to achieve value than other solution types.

Insurance-vertical solutions are similar to general-purpose solutions but with additional training and design components specifically applicable to the insurance industry. These solutions are applicable to most functions of an insurer, from underwriting to claims, and require only moderate investment in training to achieve results in the short term.

Fit-for-purpose solutions are targeted even more narrowly than insurance-vertical solutions. These solutions will generally be able to achieve accuracy―and value―very quickly when applied to the purpose for which they were designed and trained. However, that accuracy and near-immediate return can be fairly expensive. A subset of fit-for-purpose solutions do not come with pre-trained models. Those solutions will require a substantial time investment to train them with the appropriate data and consequently take longer to reach their potentially higher accuracy.

Insurers should carefully evaluate fit-for-purpose solutions for specific use cases and explore horizontal solutions to address a wider range of enterprise needs. No matter what kind of solution an insurer pursues, exciting new technology shouldn’t be an excuse to skip a cost-benefit analysis while being mindful of potential delays in achieving accuracy.

For a more comprehensive understanding of the IDP space, read our report, Intelligent Document Processing: Ingestion Powered by AI, August 2023.