6 insurance use cases for large language models

Advancements in technology are revolutionizing the operations of organizations, including insurance companies. Reinsurers, agents, brokers and MGAs have adopted advanced cognitive solutions to enhance efficiency and meet the evolving expectations from policyholders. Now, with the proliferation of generative artificial intelligence, insurtechs and insurance organizations are looking for ways to further harness the potential of powerful AI tools and language models.

Insurance companies continuously seek ways to manage risks more effectively, improve accuracy, automate policy reviews, and enhance underwriting efforts. And generative AI tools offer a clear solution to these challenges. However, understanding prompt engineering is crucial to fully capitalize on the possibility of solutions like ChatGPT, Bard, Alpaca, and LLaMA AI.

Prompt engineering involves constructing effective inputs for a generative AI tool to achieve desired outputs. In the case of platforms like ChatGPT, prompt engineering focuses on improving the queries posed to the language model to obtain precise and desired responses.

Let us explore several use cases where prompt engineering can benefit insurers.

1. Streamlining claims processing

Claims processing is a complex workflow that requires careful investigation and timely action. Manual claim-handling processes often lead to delays, customer frustration, and increased expenses for insurers. However, insurers can automate extracting critical information from claim forms and documents through AI tools and prompt engineering. By creating well-crafted prompts, language models can accurately extract relevant details, such as policy numbers, policyholder information, claim amounts, evidence, and incident descriptions. 

According to McKinsey, a digital claims function can result in a 20% increase in customer satisfaction scores and a 25-30% reduction in claims expenses.

2. Enhancing fraud detection

Insurance fraud poses a significant financial burden for insurers, with annual losses reaching billions of dollars. Leveraging AI and prompt engineering, insurers can analyze claim descriptions, policy details, and evidence to detect patterns and identify high-risk or suspicious claims. By creating specific prompts, language models can aid investigators in focusing their efforts on potentially fraudulent activities.

3. Driving efficiency gains

Prompt engineering offers a valuable benefit in terms of efficiency gains. By designing effective prompts, insurers can automate repetitive and time-consuming tasks using language models—for example, slip comparison, which typically requires extensive manual effort. Insurers can provide the slips to the model and wait a few seconds for it to compare both slips side-by-side automatically. The model highlights all the fields that are different and those that are the same, allowing insurance professionals to focus on more complex and value-added tasks, eventually leading to efficiency gain.

4. Enhancing customer success

Prompt engineering will play a crucial role in enhancing the policyholder experience. Well-curated prompts enable language models to provide accurate and detailed information to customers, guiding them through policy and coverage details, claims procedures, and frequently asked questions. Integration with existing customer success systems, such as chatbots and virtual assistants, can facilitate seamless interactions and help provide personalized responses to policyholder queries, including premium calculations, renewal dates.

5. Facilitating underwriting and policy issuance

One of the biggest challenges that the insurance industry faces today is the significant backlog of applications waiting on underwriters for processing. As a result, there are delays in responses and customers have a negative experience. Prompt engineering can address this challenge by guiding language models to generate policy documents based on customer inputs. By framing prompts to gather relevant data, insurers can automate the policy generation process, ensuring accuracy and efficiency. Moreover, prompt engineering within the underwriting process can help prioritize regulatory requirements and compliance standards. Customized prompts can allow underwriters to gather the necessary information for ensuring the policy adheres to applicable regulations, such as state laws or industry-specific guidelines.

6. Accurate risk assessment

Language models can help insurers bolster risk assessment by analyzing vast data and evaluating potential risks associated with insurable assets. With prompt engineering, insurers can consider a wide range of factors such as historical data, customer information, market trends, and demographics to ensure accurate premium calculations. That would enhance overall operational effectiveness and provide faster responses to brokers and policyholders. Additionally, prompt engineering can help language models to continuously learn and adapt to the newly developed data, helping insurers stay up to date with evolving risks and ensure risk evaluation is comprehensive.

Conclusion

Prompt engineering is pivotal to effective language model utilization in the insurance space. By embracing prompt engineering, insurers can achieve operational excellence, ensure regulatory compliance, perform accurate risk assessments, and attain significant efficiency gains. 

Insurers can refine the accuracy, relevance, and responsiveness of language models through iterative prompt engineering based on user interactions and feedback. Fine-tuning prompts is a quick and efficient process that allows language models to continuously learn and adapt to evolving user needs. Even slight changes in prompts can lead to incremental improvements over time.