Generating automated insurance documents using the latest NLP models

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June 30, 2023


AI / technology


Generating automated insurance documents using the latest NLP models

NLP Models: The latest advancements in Artificial Intelligence (A.I) have overflowed to almost all the leading industries in operation today. On the other hand, the insurance industry is in a state of flux as it is going through a transformation with strong undercurrents being powered by digital innovation. Smart insurance is a paradigm that exploits the huge amount of generated data in the process to make itself inclusive as much as possible. Furthermore, it encompasses the ability to apply internal, external, and behavioral data to add value across the insurance lifecycle—and the possibilities are endless. For example, an increasing number of insurers are tracking data from wearables picked by sensors embedded in the devices, with prior permission from users about their fitness status to decide the rate to offer insurance.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that deals with the interaction between computers and human (natural) languages. Developers use NLP to create applications that understand and interpret human language and naturally respond to humans. With the proliferation of the written word – text in online news, blogs, reviews, as well as emails, chats and other internal documents – this new technology is benefiting many companies both online and offline.

GPT-3, a Generative Pre-training Model, is the most recent language model from the OpenAI research lab team. They announced GPT-3 in a May 2020 research paper, “Language Models are Few-Shot Learners.”, which has been demonstrated to interact with humans, including writing human-level stories, poems and even research papers.

Smart Insurance – NLP Models

After tremendous information and communication technology development, the insurance industry tended to benefit from this development. Today, information technology is widely used to communicate with brokers, process insurance documents and analyze the market. According to a recent survey, adopting the insurance industry technology through smart insurance services will achieve financial savings ranging between 10-15% of the total general and administrative expenses and expenses incurred annually by insurance companies while enhancing public revenues and achieving good profits. Furthermore, smart insurance reduces distribution costs ranging from 12-26% of the value of insurance premiums.

A by-product of automating the insurance process is that it will reduce business and internal management by conducting smart business, which implies improving management levels and reducing the real completion time.

Insurance has become one of the important branches of the economy, which through the turnover achieved on a global level, amounted to about 4,778,248 million US dollars in the year 2022, with a share of 6.7% of the global gross domestic product and represented a rising percentage year after year in the GDP.

Therefore, transforming the insurance process from manual work to an automated process with minimal human intervention saves company expenses and contributes to the world economy at large.

NLP Models in the insurance industry

The insurance industry deals with an extensive amount of text documents, and manual review is both time consuming and prone to errors. NLP can automate the extraction of relevant information from such text documents, classify documents, and use relevancy scores and sentiment scores to rate documents for risk or urgency.

After NLP processes and scores the documents, it can automatically route them to appropriate departments or analysts for further review. This active process ensures efficient handling of the documents and streamlines the workflow for effective decision-making. The automated document workflows, extraction, and categorization play a significant role in reducing the processing time of insurance claims and responding to support tickets, while also increasing the accuracy of the processing.

A number of insurance start-ups, such as Friendsurance, Lemonade, and Policygenius, have attracted large investments for their smart services. Most insurance start-ups involved in distribution have sites with well-developed content, often accompanied by the application of AI or robo-advice based on NLP. These improvements intend to enhance the customer experience and reduce commissions/fees when selling products, although they will likely incur a higher initial fixed cost.

Transforming Insurance Industry with NLP and AI Technology

BIMA uses mobile technology to provide insurance services in developing and emerging markets, which the technology permits with lower entry costs. Many developing countries, particularly in Africa, widely use mobile phones for telecommunications, banking, and payment services.

Cloud service providers like guidewire and cloud insurance provide various SAAS features, which NLP-based models back for deducing crucial elements like the amount of revenue, financial history, and risk assessment for their clients.

Haptik, an AI service provider, recently developed an intelligent virtual assistant for Zurich insurance called Zuri to help insureds resolve their policy-related problems, such as claims processing. Haptik’s cutting-edge NLP chatbot technology currently enables Zurich Insurance to handle around 85% of client enquiries automatically, with 70% of all inquiries being fully automated, independent of human intervention.

Uncertainties and Challenges

While the strategic utilization of NLP and machine learning models has streamlined immense manual work in the insurance industry, it is important to acknowledge that they may exhibit some bias since humans, who inherently possess psychological biases, curate the training data.

Training large language models is cumbersome and requires a lot of time, which can contribute to a high amount of carbon emission from the data centers. In a recent paper, Strubell outlined that training a neural architecture search-based model with 213 million parameters would generate more than five times the lifetime carbon emissions of the average car. It is worth noting that GPT-3, with its 175 billion parameters, and the rumored next-generation GPT-4, with 100 trillion parameters, would likely have even larger carbon footprints if trained.

What’s Next?

The language models employed for smart, automated insurance presents a lot of potential, and with the future incremental developments in NLP, with industry giants backing the process, it is sure to prosper and open up new possibilities.

According to people familiar with its plans, Microsoft is investing as much as $10 billion in OpenAI, the creator of the artificial intelligence bot ChatGPT. With such hefty investments, the world would witness a revolution in NLP models, including the insurance sector.





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