By Anika Khanolkar
Generative AI has been around since the mid-1900s, but it has only recently been picking up pace. Numerous repetitive operations that would normally need manual labor are automated, which increases creativity and efficiency of the activity. As a result, it is fundamentally altering how corporate tasks are conducted. There are also many risks associated with generative ai. Many companies are worried about employees sharing sensitive information that will subsequently be uploaded on the internet and accessable to all.
Some general applications of Generative AI are given below:
Several processes like risk evaluation, policy creation and decision making are heavily reliant on data in the Insurance industry. The advancement of digital technology has essentially created a mother lode of data for insurance companies to easily access. In this industry, generative ai can increase the accuracy of risk assessment if it extracts data from the “pot of gold” to create prototype samples that can be included in training datasets for predictive models. Complex cases cannot be analyzed by the models since there are relatively few past samples to draw from. Companies will need to maintain readily available agents to handle such issues. Complete automation of the process will take time to fully develop. LLMs will be taught based on a small dataset compared to the overal data population which can lead to biases while generating responses. Insurers analyze a lot of sensitive data which has to be regulated while providing to the LLM.
Key areas where LLMs are used in the Insurance Sector
- Enhancing Customer Service: They aid in obtaining thorough answers to user questions concerning the insurance policy’s terms and conditions. Furthermore, the insurance policy comparison model facilitates a thorough comparison of various insurance plans for better decision-making.
- Document Analysis: The LLMs will be more efficient in extracting relevant information from large documents and ordering it concisely. Large volumes of data, such as claims history and demographic data, can be analyzed using LLMs to anticipate future claims.
- Assessing Risk Profiles: Risk assessment involves handling large amounts of data like medical records, tax records and other personal documents. It would take significantly longer for a human to manually filter through them to create a risk profile than it would for an LLM. It will be less labor-intensive and more efficient with a LLM performing the task.
- Underwriting: Insurers may tailor plans for each client’s needs by using predictive analytics to comprehend risk better and by offering real-time data for quotations on demand.
The whole insurance sector relies on interactions between the consumer and the firm to provide certain assurances in the event of specified circumstances occurring. Insurance companies gain a competitive edge by tailoring policies to the customers’ specific needs and preferences. Language models will help expedite the process of reviewing documents and obtaining sufficient data to offer to the client. These models may be trained using datasets derived from prior client inquiries. Customer inquiries can also be sorted more rapidly into the various categories of claims for easier processing. AI that is led by an LLM could assist with automated claims processing by collecting important information from claim forms, policy paperwork, and supporting evidence.
Underwriting is a key part of the insurance process. It’s all about figuring out what potential policyholders are at risk of. Manual underwriting takes more effort from both the analyst and the customer; an extensive amount of documentation must be submitted and thoroughly sifted through. This increases the risk of reaching an inaccurate conclusion due to human error. Large language models, on the other hand, can analyze massive amounts of data at once, such as social media, financial information, and medical records. Integrating language models into the underwriting process can assist insurers in making more informed judgments, reducing bogus claims, and setting rates more precisely. Because the models will learn from prior failures, their efficiency will continue to improve. The time saved by the underwriters may then be used to improve the entire customer service experience for the clients.
Areas where LLM can improve in the future
Claims Fraud detection:
An insurance firm must verify the risk nearly immediately. Without access to external data to verify an applicant’s identity and character, there is a greater chance of making a mistake. As time progresses, the dataset which the LLMs work off will be larger providing them with more data to assess the situation.
Insurance Pricing:
The insurance pricing depends on a variety of changing factors. Calculating the pricing based on personal data the client provides the LLM with is still a work in progress. When the LLM evaluates an adequate number of clients, it will be able to provide an accurate price.
Easier extraction and ordering of data:
At this moment, most LLMs can extract text-based data with ease. When it comes to extracting structured data, it faces quite a few challenges. Pattern identification is something that needs to be improved. Often, prompts involving a complicated analysis do not obtain an accurate answer due to the LLM’s incapability to comprehend it correctly.
Conclusion
The LLM output will become more accurate and refined over time as the amount of textual data available will only keep increasing from here on out. According to Gartner, the worldwide market for AI software will exceed $135 billion by 2025, with the BFSI sector accounting for nearly 25% of the whole market. Generative AI must be used as assistance rather than as a replacement for humans as humans can assure accuracy whereas the AI can make mistakes due to lack of data. Nevertheless, these LLMs will always give the insurers an advantage.
References:
https://www.twoimpulse.com/en/insights/large-language-models-insurance