LLMs for Insurance: Opportunities, Challenges and Concerns

1. Introduction

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, especially within the financial services sector. These models, powered by deep learning and extensive training on diverse text data, excel in understanding and generating human language, which is pivotal for numerous Natural Language Processing (NLP) tasks [1,2,3]. Their capabilities extend from simple text generation to complex tasks like sentiment analysis and automated decision-making.

As the financial services industry continues to embrace technological advancements, LLMs are becoming increasingly integral in fostering innovation, particularly within the insurance sector [4,5,6]. These powerful models streamline complex processes by automating tasks that traditionally require human intelligence, such as processing claims and personalizing customer interactions. This automation significantly enhances operational efficiency and boosts customer satisfaction by delivering faster and more accurate services. For example, the integration of sentiment analysis and advanced risk assessment algorithms allows insurance companies to tailor their services with unprecedented precision [7,8,9,10]. By analyzing customer sentiment and behavior patterns, these technologies enable insurers to design insurance packages that are better suited to individual needs and risk profiles.

However, implementing LLMs in the insurance industry is fraught with challenges, primarily due to the sensitive nature of the data involved and the high standards required for accuracy and fairness. The insurance sector deals with large volumes of personal and financial information that must be handled with the utmost care to maintain privacy and security. Ensuring the integrity and confidentiality of this data while utilizing LLMs poses significant challenges [11,12]. Furthermore, there is a critical need to develop LLMs that make unbiased decisions. Since these models learn from historical data, there is a risk that they might perpetuate existing biases unless carefully managed. This necessitates robust mechanisms to monitor and refine the decision-making processes of LLMs continuously. Moreover, maintaining transparency in AI-driven decisions is vital to building and retaining trust among consumers. Insurers must provide clear explanations for decisions made by AI systems [13], especially when these decisions have significant implications for policyholders.

Given these complexities, the potential for LLMs in insurance also raises several concerns. As these models learn from vast datasets, there is a risk of perpetuating existing biases or creating new ethical dilemmas, such as discrimination in premium calculations or claim approvals [14,15,16]. The future applications of LLMs will need to address these issues carefully, ensuring that technological advancements enhance the industry without compromising ethical standards and customer trust. As the financial services industry continues to embrace technological advancements, LLMs are becoming increasingly integral in fostering innovation, particularly within the insurance sector [17]. These powerful models streamline complex processes by automating tasks that traditionally require human intelligence, such as processing claims and personalizing customer interactions. This automation significantly enhances operational efficiency and boosts customer satisfaction by delivering faster and more accurate services. For example, integrating sentiment analysis and advanced risk assessment algorithms allows insurance companies to precisely tailor their services. By analyzing customer sentiment and behavior patterns, these technologies enable insurers to design packages better suited to individual needs and risk profiles.

However, implementing LLMs in the insurance industry is fraught with challenges, primarily due to the sensitive nature of the data involved and the high standards required for accuracy and fairness [18,19]. The insurance sector deals with large volumes of personal and financial information that must be handled with the utmost care to maintain privacy and security [20,21,22]. Ensuring the integrity and confidentiality of this data while utilizing LLMs poses significant challenges. Furthermore, there is a critical need to develop LLMs that make unbiased decisions. Since these models learn from historical data, there is a risk that they might perpetuate existing biases unless carefully managed. This necessitates robust mechanisms to continuously monitor and refine the decision-making processes of LLMs. Moreover, maintaining transparency in AI-driven decisions is vital to building and retaining trust among consumers. Insurers must provide clear explanations for decisions made by AI systems, especially when these decisions have significant implications for policyholders.

2. Development of Large Language Models

Large Language Models are models trained with deep learning techniques to understand and generate human language [23,24]. The core function of a language model is to predict the next word or continue a text sequence, serving as the backbone for NLP tasks [25]. These NLP tasks range from text classification and language translation to sentiment analysis, and they rely on sophisticated language models to process and interpret extensive textual data.

Early language models relied on statistical methods like n-grams to predict the next word based on word sequence frequency. Though effective on limited vocabulary tasks, these models fell short in long-range dependencies and complex sentence structures. Then, the evolution of machine learning, especially with neural networks like Recurrent Neural Networks (RNNs) and later the Transformer architecture, greatly enhanced the ability to handle long sequences [26,27,28]. With a self-attention mechanism, transformers can process all sequence elements simultaneously. This is necessary for effectively capturing long-range dependencies needed for understanding and generating complex texts [29].

Current LLMs have demonstrated exceptional performance in semantic understanding, sentiment analysis, dialogue systems, and text generation [30,31]. Their success highlights their critical role in advancing AI technology, as they enhance human-computer interaction and automate information processing [32,33,34]. LLMs represent a significant leap in NLP, driving advancements in modern AI and paving the way for broader AI adoption and future applications.

The foundational technology of LLMs is the Transformer architecture [35,36]. This architecture revolutionized NLP by replacing recurrent layers with a series of attention mechanisms that allow the model to weigh the importance of different words in a sentence, regardless of their positional distance. Transformers consist of multiple layers of self-attention and feed-forward neural networks, enabling them to process exceptionally long data sequences and capture complex dependencies within them [37,38]. To build LLMs from the Transformer base, the architecture is scaled up by increasing the number of layers, attention heads, and parameters, allowing the model to learn from a vast corpus of textual data and capture nuances that smaller models might miss. This scalability is crucial as it enhances the model’s ability to perform a wide range of linguistic tasks, from real-time translation to generating coherent text over extended passages. Distributed computing further facilitates the transition to LLMs by providing the necessary computational power to process large volumes of data across thousands of GPUs or TPUs. This not only accelerates the training process but also allows for larger batches of data, critical for model convergence on such scales. Increasing training volumes improves generalization, minimizes overfitting, and enhances the model’s performance across a broader range of tasks, capturing more detailed and varied linguistic patterns. These technological advancements significantly set LLMs apart from their predecessors, pushing the boundaries of what is achievable in artificial intelligence and natural language processing.

The training of LLMs typically follows a structured approach. First, the model undergoes pre-training on a vast corpus of textual data to grasp the basic rules and patterns of language [39,40,41]. During this stage, the model learns to predict vocabulary, construct sentence structures, and understand language usage across various contexts by analyzing thousands of text samples [42,43,44]. Further, to enhance performance for specific applications, the model undergoes fine-tuning on particular downstream tasks such as text summarization, question answering, or sentiment analysis [45,46,47,48]. This fine-tuning phase enables the model to adapt to the specific demands of individual tasks, improving accuracy and effectiveness in targeted areas. LLMs boast fundamental capabilities like text generation, comprehension, and transformation, supporting a wide array of linguistic tasks. These capabilities make them powerful and versatile tools for applications, including machine translation, content summarization, automated question answering, and dialogue systems [49,50,51,52].

As technology evolves, the scope of LLM applications has vastly expanded beyond basic text handling. Modern LLMs not only manage text generation and comprehension but also engage in more complex cognitive tasks such as sentiment analysis and automatic summarization [53,54,55]. These functions enable the model to detect emotional tendencies in text or extract key information from lengthy documents. Moreover, LLMs are developing capabilities in logical reasoning and common-sense judgment, mimicking human thought processes to some extent and handling intricate reasoning tasks [56,57,58]. The latest developments include multimodal tasks like joint image and text generation and comprehension, enabling models to process and generate visuals related to textual information. Interactive learning marks another advancement for LLMs, with models continuously adjusting and optimizing their performance and outputs based on user feedback. This adaptability enhances the model’s practicality and impact as an AI application in various fields.

Currently, LLM-related studies has seen significant breakthroughs, including OpenAI’s GPT series, Google’s BERT and T5, and Gemini [59,60,61,62]. These models have garnered widespread attention in academia and have proven highly effective in commercial applications, such as customer service, content recommendation, and automated editing. For example, Amazon [63] uses conversational bots powered by LLMs to enhance user interaction and improve customer service. Character.AI [64,65] leverages advanced LLMs to create engaging chatbots that provide human-like interactions for a unique conversational experience. These examples demonstrate that LLMs drive innovation across diverse industries and have become crucial in transforming modern applications.

3. Application of LLMs in the Fintech Industry

The financial services industry is a cornerstone of the modern economic system, encompassing key areas such as banking, insurance, investment management, payment systems, and financial consulting [66,67,68]. While these sectors vary in their service content and operational models, their core functions revolve around managing money and assets, facilitating capital flow, risk management, and delivering investment returns. These industries share a heavy reliance on large-scale data processing capabilities and advanced information technologies to handle complex financial transactions and vast amounts of customer data [69,70,71,72]. An effective customer service system is critical to their success, directly impacting customer satisfaction and the market reputation of businesses. Given the financial operations involved, risk management is also crucial, requiring precise risk assessment and management strategies to prevent potential financial losses and legal risks. The financial services industry faces common challenges in enhancing the efficiency of transactions and data processing, reducing operational costs, while also ensuring the security and accuracy of services. The complexity and scale of operations in this industry often involve cross-border transactions and multi-currency handling, making high-frequency transactions a norm. Moreover, these sectors must comply with strict regulatory and compliance requirements, including anti-money laundering regulations, data protection laws, and other financial regulatory standards. Ensuring data security is another major challenge, as the financial services industry is a high-risk target for cyber-attacks [73,74,75]. Any data breach or security flaw can lead to significant financial losses and damage to reputation. Therefore, improving customer experience, optimizing risk management, and enhancing data security are pressing issues that these industries need to address.

Facing these challenges, LLMs can leverage their advanced text comprehension and generation capabilities to help the financial services industry address multiple common issues. For instance, in banking, LLMs can automate customer inquiry services by understanding and responding to customer queries, enhancing service efficiency and customer satisfaction [76,77]. In the insurance sector, LLMs assist in automating claim processing by quickly and accurately analyzing claim requests and related documents, significantly speeding up the process and improving accuracy. In payment systems, LLMs provide smarter fraud detection by analyzing transaction patterns and behavior to effectively identify potential fraudulent activities and reduce losses. These applications not only boost operational efficiency but also enhance customer experience and help businesses cut costs and risks.

Figure 1. Caption

Figure 1. Caption

As LLM technology matures, its ability to understand complex texts, generate high-quality responses, and adapt to various financial environments will significantly improve. This means LLMs will be better equipped to handle complex queries and transactions, meet customer needs more effectively, and comply with increasingly strict regulatory demands:

• LLMs greatly enhance service efficiency and quality in automated customer inquiry and support. They can understand specific customer questions and generate precise, personalized responses [78,79], boosting customer satisfaction and loyalty.
• LLMs play an essential role in risk assessment and compliance monitoring. Analyzing vast amounts of transaction data and identifying patterns helps detect potential risks and fraud early [80,81]. This enables financial institutions to take preventive measures, reduce losses, and ensure regulatory compliance.
• LLMs can automatically process and analyze large volumes of complex financial documents and transaction data, reducing the human workload and improving processing speed and accuracy [82,83]. This capability is especially critical for financial institutions dealing with high-frequency transactions and cross-border activities.
• LLMs drive financial innovation and new product development. By analyzing market trends and consumer behavior data, LLMs help financial institutions design and promote new financial products and services, enabling them to respond more effectively to changing market demands [84].

4. Application of LLMs in the Insurance Industry

The insurance industry forms the backbone of the financial services sector, crucial for managing risk and providing economic security [85,86,87,88]. The industry broadly splits into life insurance and non-life insurance categories. Life insurance, including health policies, primarily aims to offer financial support to individuals and their families in the event of death or health issues [89]. Non-life insurance covers property, auto, travel, and liability insurance, helping individuals and businesses mitigate financial losses from physical damage, accidents, and natural disasters [90]. By offering these products, the insurance sector helps transfer risk and ensures necessary financial support and recovery capabilities in the face of uncertainty and potential economic losses, thus supporting the stability and growth of society and the economy.

Meanwhile, the insurance industry, known for its protective role and extensive risk assessment, carries unique complexity and demands high predictiveness [91,92]. Beyond the usual challenges of the financial services sector—like data processing and customer service—the insurance industry faces additional hurdles. These include maintaining accurate risk assessments, detecting insurance fraud, and meeting the growing demand for customized insurance policies.

As the potential of LLMs is tapped, advanced models like ChatGPT are reshaping various aspects of the insurance industry [93,94,95]. These models significantly boost operational efficiency, customer interaction quality, and internal process management by utilizing technologies like reasoning, sentiment analysis, and text classification. LLMs particularly impact customer service, claims processing, and risk management.

In customer service, LLMs like ChatGPT handle a wide range of interactions in real-time, offering instant answers to common questions, assisting with policy details, and guiding customers through claims processes—all in a natural and engaging conversational style [96,97,98,99]. This not only raises customer satisfaction but also enhances service efficiency. By analyzing customer data, LLMs provide personalized interactions that improve service quality.

In claims processing, LLMs equipped with tabular learning capabilities expedite the process by automating initial assessments and document handling, employing text mining and knowledge extraction capabilities [100,101]. This automation shortens processing times and reduces human errors, making the entire claims process more efficient and customer-friendly.

Moreover, LLMs show great promise in risk management. LLMs effectively identify potential risks and fraud by analyzing extensive historical data and behavioral patterns and integrating time series analysis and anomaly detection technologies [102,103,104,105]. This helps insurance companies optimize product design and pricing strategies, better meeting market demands and customer expectations.

The insurance industry currently faces numerous challenges, including delayed customer service responses, complexity in the claims process, and inaccuracies in risk assessment. LLMs’ advanced technology offers effective solutions to these issues. With their powerful text processing and data analysis capabilities, LLMs significantly enhance the quality and efficiency of insurance services.

• Improving Customer Service Response Times: Traditional customer service often suffers from lengthy response times and low efficiency. With LLM’s advanced text understanding and generation capabilities, real-time, automated customer responses become possible. LLMs can instantly answer customer queries and provide detailed information about insurance policies, using their deep understanding of natural language [106,107,108,109]. This immediate responsiveness greatly enhances customer satisfaction, optimizes the customer experience, reduces waiting times, and boosts overall service efficiency.
• Streamlining the Claims Process: The claims handling process is typically complex and time-consuming, requiring substantial manual document processing. LLMs utilize their natural language processing capabilities to automate the processing and review of claim documents, identify key information, and accelerate the document workflow [110,111,112,113]. This automation reduces the workload on human staff and increases processing speed and accuracy, making the claims process more efficient and customer-friendly.
• Enhancing the Accuracy of Risk Assessment: Traditional risk assessment methods often rely on outdated data and limited analytical capabilities, which can lead to inaccurate risk predictions. LLMs, with their pattern recognition and data analysis capabilities, analyze vast amounts of historical data and behavioral patterns. Through precise data analysis and trend prediction, they enhance the accuracy of risk assessments. More accurate risk assessments help insurance companies optimize their insurance products and pricing strategies, reduce unnecessary losses, and offer more reasonable insurance services to customers, thereby increasing customer trust and satisfaction.

5. Opportunities for LLM in the Insurance Industry

5.1. Establishing Dialogue Agents to Enhance Customer Interaction and Personalized Services

In the insurance industry, the application of LLMs offers new possibilities for customer service and personalized interactions. As shown in Figure 2, using advanced multi-turn dialogue technology, LLMs can process customer inquiries and feedback in real-time, providing quick and accurate responses. For example, by deploying LLM-based dialogue agents, these systems can continuously track the context of conversations, better understand customer needs, and provide relevant solutions. This capability ensures faster responses and more personalized and precise customer service.

Specifically, LLMs personalize service by analyzing customers’ historical interaction data, inferring potential needs from past inquiries, and proactively providing relevant information. This not only improves customer satisfaction, but also significantly improves efficiency by reducing repeated requests and misunderstandings. For example, if a customer frequently asks about car insurance details, the LLM could proactively offer the latest deals and coverage options in the next conversation, further enhancing customer experience and loyalty.

In the insurance industry, LLMs can revolutionize customer service and interaction through the use of Personalized Conversational Information Retrieval (CIR) [114,115] technology. This technology allows LLMs to tailor responses and recommendations by analyzing users’ historical conversations and behavior patterns. This involves learning from vast amounts of dialogue data to recognize preferences and needs, allowing LLMs to provide more accurate and relevant information in future interactions. For example, suppose a customer previously expressed interest or queries about a specific type of insurance. In that case, the LLM can proactively offer detailed information and the latest deals on that insurance type in subsequent conversations, achieving highly personalized customer service.

Moreover, this analysis and learning from historical data not only enhance the relevance of information but also significantly improve service efficiency. By automatically identifying user needs and behavior trends, LLMs can anticipate potential questions and needs, thus providing solutions before the customer even asks. This predictive service approach reduces customer wait times and greatly increases customer satisfaction and loyalty. For instance, by analyzing a customer’s history of conversations about insurance claims, an LLM can immediately provide updates on claim status upon the customer’s next contact, reducing uncertainty and enhancing the transparency of the overall service.

5.2. Improving Risk Assessment and Fraud Detection

LLMs enhance fraud detection and risk assessment in the insurance industry by leveraging their mathematical, textual, and symbolic reasoning capabilities. Each type of reasoning plays a crucial role in interpreting data and making informed decisions:

• Mathematical Reasoning: Mathematical reasoning enables LLMs to handle complex calculations and statistical analyses required for risk assessment [116,117,118]. For instance, LLMs can evaluate the probability of certain events based on historical data, such as the likelihood of a fraudulent claim based on past claim patterns and financial behaviors. This type of reasoning helps insurers quantify risk levels and make decisions based on statistical evidence, thereby improving the precision of risk assessments.
• Textual Reasoning: Textual reasoning involves understanding and interpreting written content within context [119,120,121,122]. In the realm of insurance, this ability allows LLMs to analyze the textual data from claims or customer interactions to identify signs of possible fraud. For example, discrepancies in incident reports or claims that deviate from typical patterns can be flagged for further investigation. Textual reasoning helps LLMs understand the nuances of language used in claims, spotting inconsistencies and anomalies that could indicate fraudulent activities.
• Symbolic Reasoning: Symbolic reasoning refers to the ability of LLMs to manipulate and reason with symbols, typically used in logical deduction and problem-solving scenarios [123,124,125]. In insurance, symbolic reasoning can be applied to automate and refine the decision-making processes. For example, by defining certain rules and conditions for what constitutes a high-risk claim, LLMs can apply these criteria systematically, checking claims against established risk indicators. This type of reasoning ensures that decisions are consistent and based on defined insurance policy parameters, reducing human error and bias in fraud detection and risk assessment processes.

These advancements not only speed up the processing of claims and assessments but also ensure a higher degree of accuracy and reliability in detecting fraudulent activities and assessing risks accurately.

Current reasoning models like o1 and table-based models such as TableGPT2 [126,127] significantly enhance fraud detection capabilities in industries like insurance by adeptly processing and interpreting structured data prevalent in claims data and financial records. LLMs with tabular processing capabilities excel in analyzing tabular data, identifying patterns, anomalies, or inconsistencies that suggest fraudulent activities by examining datasets for irregular claims patterns such as unusually high claims from specific regions [128,129,130,131]. Meanwhile, models like o1 apply advanced mathematical and symbolic reasoning to correlate diverse data points—such as the frequency of claims by a single policyholder or typical payout amounts—thereby constructing comprehensive risk profiles that aid in preemptive fraud detection. These models automate and accelerate the fraud detection process, improving the accuracy of fraud identification and reducing financial losses and operational costs for insurance companies, increasing trust and ensuring a fair and transparent claims process.

5.3. Innovating New Insurance Products and Services Through LLM Insights

Leveraging the deep learning and pattern recognition capabilities of LLMs, insurance companies can develop innovative products and services that meet the evolving market demands. The foundational technology of LLMs allows them to process and analyze vast, complex data, identifying potential patterns and trends crucial for understanding market dynamics and consumer behavior.

5.3.1. Emotional Analysis for Consumer Insight

Emotion analysis and empathetic computing are key technologies within LLMs, enabling the models to extract and interpret consumers’ emotional states from text data [55,132]. Emotion analysis assesses positive or negative sentiments, while empathetic computing delves deeper into the motives and depths of these emotions. This deep emotional understanding allows LLMs to predict consumer preferences for specific insurance products [133,134,135], such as comprehensive health insurance, sought by those expressing a strong need for security.

For example, by analyzing customer feedback on insurance claim processes, LLMs can identify aspects that satisfy or dissatisfy customers, such as processing speed, communication efficiency, or transparency in compensation amounts. This information is crucial for insurance companies as it directly guides product adjustments and service improvements to better meet customer needs and enhance loyalty.

The application of this technology extends beyond collecting and analyzing historical data; it is vital for real-time monitoring and adaptation to market changes. The combination of emotion analysis and empathetic computing provides insurance companies with a powerful tool that allows them to adjust their marketing strategies and product designs in real time, responding to changes in consumer emotions and market trends. This flexibility and adaptability are indispensable in the competitive modern insurance market, especially today when consumer demands are increasingly personalized and diverse.

5.3.2. Synthetic Data for Market Simulation

LLM’s synthetic data technology also uses advanced algorithms to generate virtual yet realistic market data that can simulate future market conditions and consumer behaviors. This capability is beneficial in the early stages of the development of new products when accurate market data are insufficient for decision making [136,137,138,139]. Synthetic data allows insurance companies to test the performance of their products in the market and identify potential risks and issues in a controlled environment before the products hit the market.

Generating synthetic data involves extracting statistical features from existing data and creating new data instances based on these features. This preserves the core characteristics and patterns of the original data while introducing a range of possible variations in the simulated datasets, thus improving the comprehensiveness and depth of testing. For example, by simulating consumer behavior during economic downturns or market upheavals, insurance companies can better understand how their products perform under complex conditions, ensuring robust product design.

The application of data synthesis provides strong support for the development, development, and marketing strategies of research for insurance products. By predicting product performance under various market conditions, companies can optimize risk management strategies, adjust product features to meet consumer needs, or identify and address potential market failures. This increases the success rate of the products and significantly reduces the economic losses that could result from products not meeting market demands. Therefore, LLM’s synthetic data technology is indispensable for modern insurance companies facing rapidly changing and highly competitive market environments.

5.3.3. Time Series Analysis for Market Trends

LLMs’ time series analysis capability is crucial for simulating market trends and consumer behavior cycles [6,140,141,142]. LLMs analyze historical market data to detect periodic changes and identify key factors driving these fluctuations, providing precise data support for pricing and positioning insurance products. Time-series analysis involves statistically examining data collected at specific intervals. LLMs leverage this capability to forecast future market conditions based on past patterns, enabling insurance companies to anticipate changes in demand and market dynamics [143]. This foresight helps them adjust strategies proactively, aligning their offerings with market needs. LLMs solve this problem effectively by processing and analyzing large datasets over time, a task traditional analytical tools often struggle with. Integrating LLMs with time series analysis improves prediction accuracy and accelerates data processing, empowering insurance companies to make faster, data-driven decisions. This capability is essential to maintain a competitive edge in a fast-paced market where predicting consumer behavior and trends determines business success.

6. Security, Risks, and Ethical Concerns

6.1. Data Privacy and Security Challenges with LLMs

Integrating LLMs into the insurance industry raises significant concerns about data privacy and security. LLMs need access to vast amounts of personal and sensitive data to train and operate effectively. This data often includes personal identifiers, financial history, and other private information that, if mishandled, could lead to serious breaches. The challenge is to implement robust encryption and access controls that protect data integrity and confidentiality while still allowing the models to function effectively. Moreover, compliance with regulations like GDPR [144,145] or HIPAA [146,147] is crucial for ensuring data is handled and protected properly.

6.2. Ethical Implications of Automated Decision-Making in Insurance

The use of LLMs for automated decision-making in insurance raises several ethical concerns. Algorithmic decisions, such as determining insurance premiums or claims outcomes, can significantly impact individuals’ lives. If not properly overseen, these decisions may lead to unfair treatment or discrimination due to biases in the underlying data or algorithms. Ensuring transparency in decision-making and providing clear explanations is essential to maintain trust and fairness. Moreover, there must be a mechanism for human review and recourse, allowing individuals to contest decisions, especially in cases where the automated system may have made an error.

6.3. Risk Management Strategies for Deploying LLMs in Sensitive Environments

Deploying LLMs in sensitive environments like the insurance sector requires careful risk management. Thorough risk assessments are essential before implementation to identify vulnerabilities and develop mitigation strategies. Continuous monitoring of LLM activities and their decisions helps detect and address issues promptly. Insurance companies must also keep LLM systems updated with the latest security practices and data protection laws to prevent exploitation. Multi-layered security measures, combining physical and software-based defenses, help ensure system resilience against attacks. Finally, regular audits and updates of LLM training datasets are necessary to maintain effectiveness, reduce bias, and prevent operational failures or reputational damage.