NLP in the Insurance Industry

The insurance industry deals with a growing volume of text documents daily. This accumulation, both internal and external, leads to massive amounts of structured and unstructured data that insurance professionals use in their decision-making processes. However, the constant influx of documents and the limited time for processing them presents a significant challenge. In response, an increasing number of insurance companies are turning to innovative technologies to restructure their processes, improve efficiency, and empower their employees to make well-informed decisions.

Underwriters, one of the industry's key knowledge workers, heavily rely on text-dense documents. Their primary tasks – risk assessment, market analysis, and policy pricing – require efficient processing of hundreds of documents. To make informed decisions, they must sift through these documents to locate relevant information. This task, vital for drawing accurate conclusions and making valid predictions, is a daunting exercise. Employees often spend up to one-third of their time seeking the information crucial for their work, with one of the largest challenges being finding the precise information necessary for analysis.

These challenges extend beyond underwriters to other roles in the insurance sector, including claims processing, risk analysis, report analysis, and customer interactions. The quality of their work directly impacts customer experience and the company's sustainability. This is especially true for team leads and executives under pressure to deliver optimal results with their teams.

In response to these challenges, many companies are following the latest industry trends, developing NLP-enabled applications designed to greatly assist their employees. The focus is on delivering faster, more accurate, and ultimately more efficient search results, enabling employees to access and analyze essential information and to make informed decisions much faster.

The technology behind these innovations is natural language processing (NLP). After Google introduced newer language models in 2018, NLP has risen to prominence as one of the most impactful and practical innovations of the past decade. With new open-source models and algorithms, developers can now build what was previously exclusive to industry giants like Microsoft, Google or Facebook. The current state of applied NLP and its use in application development allows enterprises to leverage its capabilities for text data analysis, making the cost and implications of not implementing NLP considerable.

NLP can be used for a variety of tasks in the insurance industry:

• Analysis of reports, claims, and regulatory documents
• Monitoring of economic news and publications
• Organization and screening of client documents
• Risk assessment
• Qualitative market research
• Generation of aggregate reports
• Policy compliance analysis

Modern NLP techniques include semantic search, question answering, document ranking and classification, summarization, information extraction, and more. These techniques can enhance search, identification, analysis, classification, and processing of information in the insurance sector, significantly reducing the time spent searching for documents and enabling employees to focus on their core responsibilities.

Business applications powered by NLP enable enterprise users to query information using just human language. Rather than receiving a list of documents to manually check, users get precise answers and ready-to-use summaries. The latest advancements in NLP also simplify the use of existing, readily available language models without the need to train a proprietary model or develop a new algorithm.

Insurance organizations can realize several benefits by incorporating NLP into their applications, all without incurring unnecessary costs in time or capital:

• Reducing time spent on document search
• Assisting in classifying, analyzing, and validating reports, claims, and documents
• Extracting valuable information previously hidden in vast insurance data troves
• Faster time-to-output rate while ensuring high-quality results
• Improving customer relationships and satisfaction
• Enhancing the accuracy of risk assessments
• Identifying and eliminating bias in decisions
• Efficiently restructuring existing processes to reduce time lags

For a more detailed understanding of modern NLP and to get more guidance in how to build powerful, NLP-driven solutions, please visit our website www.deepset.ai.