LLMs analyze data, improve insurance processes.
What’s an LLM?
An LLM is a text analysis tool that can finish sentences, suggest words, and write whole paragraphs based on patterns it has learned from vast amounts of text data. Imagine having a friend who always knows what you want to say next—an LLM works similarly but is powered by supercomputers on clouds and extensive training on books, articles, and websites.
You’ve probably used LLMs already
LLMs are used in various applications such as chatbots and virtual assistants like Siri and Alexa, which understand and respond to your commands conversationally. They power language translation tools like Google Translate, content creation platforms like Copy.ai, and code assistance tools like GitHub Copilot. Additionally, LLMs help summarize long texts, support customer service interactions, and provide personalized recommendations on platforms like Netflix and Amazon. These smart text-based tools and services make daily tasks easier, faster, and more efficient.
“Large”? How big?
The training set for ChatGPT-4 is vast, consisting of an immense set of data from the internet. Although specific size and content details are proprietary and not publicly disclosed by developers of ChatGPT, it is known that the model was trained on a mixture of licensed data, data created by human trainers, and publicly available data. This training data includes billions of pages covering a wide range of topics, a large collection of books across various genres and fields, numerous articles from academic journals, news sites, and blogs, as well as discussions from forums, social media posts, and other conversational data. The sheer volume and diversity of this training data enable ChatGPT-4 to understand and generate text on a wide array of topics with high accuracy and coherence.
A (somewhat) formal definition of LLM
A Large Language Model (LLM) can be formally defined using algebraic syntax notation as follows:
Let:
- \( V \) be a vocabulary set containing all possible tokens (words, subwords, or characters).
- \( S \) be a sequence of tokens \( s_1, s_2, \ldots, s_n \) where \( s_i \in V \) for \( i = 1, 2, \ldots, n \).
- \( P(S) \) be the probability distribution over the sequence \( S \).
An LLM \( \mathcal{M} \) is a function that maps a sequence of tokens \( S \) to a probability distribution:
\[ \mathcal{M}: S \rightarrow P(S) \]
Where:
\[P(S) = \prod_{i=1}^n P(s_i | s_1, s_2, \ldots, s_{i-1}; \theta)\]Here:
\[P(s_i | s_1, s_2, \ldots, s_{i-1}; \theta)\]is the conditional probability of the token \( s_i \) given the previous tokens \( s_1, s_2, \ldots, s_{i-1} \) and model parameters \( \theta \). Also \( \theta \) represents the parameters of the model, typically learned during the training phase.
In summary, an LLM is defined by:
\[\mathcal{M}(S; \theta) = \prod_{i=1}^n P(s_i | s_1, s_2, \ldots, s_{i-1}; \theta)\]Where \( S = (s_1, s_2, \ldots, s_n) \) is any sequence of tokens from the vocabulary \( V \).
A token is a basic unit of text that a language model processes. Tokens can be words, subwords, characters, or even punctuation marks. The type of tokenization depends on the specific model and its tokenization strategy. Here are some examples:
Examples of Tokens
Word-level Tokenization
- Sentence: “The quick brown fox.”
- Tokens: [“The”, “quick”, “brown”, “fox”, “.”]
Subword-level Tokenization (e.g., Byte Pair Encoding)
- Sentence: “unhappiness”
- Tokens: [“un”, “hap”, “pi”, “ness”]
Character-level Tokenization
- Sentence: “AI”
- Tokens: [“A”, “I”]
Mixed Tokenization
- Sentence: “I love AI!”
- Tokens: [“I”, “love”, “AI”, “!”]
LLMs for Insurance
Large Language Models (LLMs) are promising tools for transforming the insurance industry by improving risk assessment and claim processing.
In actuarial science, LLMs analyze vast amounts of textual data from medical records and social media to enhance mortality predictions and refine premium calculations.
In claim processing, LLMs automate the review of accident reports and repair estimates, quickly extracting relevant information and detecting fraud. This leads to faster, more accurate claim resolutions and better customer service, making the insurance process more efficient and reliable.
LLMs in Life Insurance Actuary
One key use case is the analysis of unstructured data, such as customer health records, policy documents, and historical claims data. By leveraging LLMs, actuaries can extract meaningful patterns and insights from vast amounts of textual information, aiding in more precise risk assessment and premium calculation. Additionally, LLMs can assist in the development of predictive models for mortality and morbidity rates by analyzing trends and anomalies in historical data, thus improving the accuracy of life insurance pricing and underwriting decisions.
Furthermore, LLMs can streamline the process of regulatory compliance and reporting. Actuarial reports and compliance documentation often require thorough analysis and synthesis of complex regulatory texts. LLMs can automate the extraction of relevant regulatory guidelines, ensuring that actuarial practices align with current legal standards. This not only reduces the manual effort involved but also minimizes the risk of non-compliance, ultimately contributing to more robust and reliable actuarial processes in the life insurance sector.
LLMs in Car Insurance Claim Processing
In the car insurance industry, LLMs can handle claim processing by automating the extraction and interpretation of information from claim forms, accident reports, and repair estimates. This automation enables better and faster assessment of claims, reducing the time and cost associated with manual review. For instance, LLMs can identify critical details such as the extent of vehicle damage, liability, and policy coverage, facilitating quicker decision-making and settlement of claims.
LLMs can also improve customer service by providing chatbots and virtual assistants that handle claim-related inquiries. These AI-driven systems can understand and answer customer queries in natural language, offering guidance throughout the claim process. This improves customer satisfaction and at the same time frees up human agents to focus on more complex tasks. Additionally, LLMs can detect fraudulent claims by spotting textual inconsistencies and patterns, protecting the insurance company’s resources and ensuring fair processing of legitimate claims.