1. Introduction
1.1. Background and Motivation
The research question that this paper aims to address is: how can large language models (LLMs) be effectively applied within actuarial work? This question will be explored through a review of existing literature in this section, followed by a detailed discussion of their application to the claims process, as well as a high-level discussion on applications in other areas. To assist actuaries in identifying applications of LLMs, a decision framework is provided. Subsequent case studies will demonstrate practical applications of LLMs in various actuarial areas, accompanied by code. The paper then concludes with a brief discussion of the risks and ethical considerations associated with using LLMs and a call for further research.
Actuarial work has historically relied on structured datasets of manageable size. However, in today’s data-driven world, the volume and complexity of data available to actuaries have increased exponentially, presenting new challenges and opportunities for the industry. Moreover, actuaries’ skills as risk professionals expand their impact to areas outside of valuing liabilities and deriving premiums, often involving them in areas such as risk management, marketing, underwriting, and product development.
With the advent of big data and advances in technology, the volume and complexity of data available to actuaries have increased significantly. While traditional actuarial methods have served the industry well in the past, they are not always equipped to handle the vast amounts of unstructured data available today. This presents an opportunity for actuaries to pivot towards new approaches that can more efficiently and effectively process and analyse information. By embracing new methods and technologies, actuaries can expand their analytical capabilities and make more informed decisions based on the insights derived from these datasets. Furthermore, as advanced analytics projects become more commonplace in the industry, actuaries will need to be able to consume data produced by these projects and incorporate it into their work.
One such approach is the use of LLMs. LLMs are a type of artificial intelligence (AI) model that has been increasingly used in recent years due to an explosion in research resulting from the increased availability of affordable computation and the free availability of massive electronic corpora as training material. LLMs are trained on massive datasets of text, which enables them to learn complex patterns and relationships in language. Notable examples of LLMs include the GPT-3 (Generative Pre-trained Transformer), GPT-3.5, and GPT-4 models (Brown et al., Reference Brown, Mann, Ryder, Subbiah, Kaplan, Dhariwal, Neelakantan, Shyam, Sastry, Askell, Agarwal, Herbert-Voss, Krueger, Henighan, Child, Ramesh, Ziegler, Wu, Winter, Hesse, Chen, Sigler, Litwin, Gray, Chess, Clark, Berner, McCandlish, Radford, Sutskever and Amodei2020; OpenAI, 2023), which were released by OpenAI from 2020 through to 2023 and have been used for a wide range of applications. Another example is BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., Reference Devlin, Chang, Lee and Toutanova2019), a popular LLM developed by Google, which has been used for tasks such as sentiment analysis, question answering, and text classification.
Regarding pre-trained transformer models in their earlier states, Min et al. (Reference Min, Ross, Sulem, Pouran, Veyseh, Nguyen, Sainz, Agirre, Heinz and Roth2023) provide a comprehensive survey on the use of pre-trained transformer-based language models in various natural language processing tasks. They discuss different approaches including pre-training, fine-tuning, and prompting. The paper highlights the versatility of these models but also key limitations, suggesting areas for future research such as improving efficiency, understanding model behaviour, and enhancing robustness. A comprehensive bibliometric review of over 5,000 papers on LLMs research from 2017 to 2023 is given in Fan et al. (Reference Fan, Li, Ma, Lee, Yu and Hemphill2023). They identify key research themes, collaboration patterns, and discourse trends in the field. The paper finds that over half of all research focuses on LLMs themselves and their algorithms. The areas where most applied research occurs are social and humanitarian applications and medical and engineering applications. Those interested in a highly detailed and comprehensive overview of LLMs are referred to Zhao et al. (Reference Zhao, Zhou, Li, Tang, Wang, Hou, Min, Zhang, Zhang, Dong, Du, Yang, Chen, Chen, Jiang, Ren, Li, Tang, Liu, Liu, Nie and Wen2023).
Research into applications of LLMs in actuarial work is limited given its recent widespread introduction. Troxler and Schelldorfer (Reference Troxler and Schelldorfer2022) demonstrate the potential of natural language processing in actuarial applications. They present case studies showing how unstructured text data can be structured for classification and regression tasks. The paper also explores domain-specific and task-specific fine-tuning of transformer models, suggesting that further improvements can be achieved through tailored model tuning. Dimri et al. (Reference Dimri, Paul, Girish, Lee, Afra and Jakubowski2022) present a detailed system for auto insurance claims management that leverages both structured and unstructured data, using what they term insurance-based language models. The system predicts claim labels and routes them to appropriate domain experts, improving efficiency and customer satisfaction. Specifically regarding ChatGPT, Biswas (Reference Biswas2023) explores the use of ChatGPT in the medical insurance industry. Some considerations relate to LLM’s potential in risk assessment, fraud detection, reducing human error, and enhancing customer service. However, it also notes potential challenges, including data privacy concerns, lack of human empathy, and dependence on data quality.
Hofert (Reference Hofert2023) engages in a scholarly discussion with ChatGPT. The authors explore its understanding of key concepts in quantitative risk management relevant to actuarial practice. The findings indicate that ChatGPT is proficient in non-technical aspects of risk, such as explanations of various types of financial risk. However, it falls short in more technical aspects, often providing inaccurate or incorrect mathematical facts, sometimes in subtle ways. The authors offer guidance on when to consult ChatGPT for insights into quantitative risk management in actuarial practice and highlight situations where it should not be relied upon. In Hofert (Reference Hofert2023) in discussions with ChatGPT related to correlation pitfalls in risk management, the authors find it lacks the mathematical depth required to fully comprehend the underlying concepts or avoid certain pitfalls. Despite this limitation, the paper suggests potential ways to leverage ChatGPT as a tool for enhancing the learning process in this area.
Given that LLMs are a new technology, research into the applications of LLMs to insurance and actuarial work is in its infancy. This paper aims to address this gap by presenting a discussion on how LLMs can be applied to a claims management process and by providing case studies demonstrating the practicality of using LLMs in actuarial work. This serves as a broader exploration of the potential applications of LLMs in insurance and actuarial work than those considered above, where earlier natural language processing models are mostly used. Additionally, this paper explores the programming, workflow, and problem-solving challenges that actuaries face in their day-to-day work and how LLMs can help to address those challenges. Our findings suggest that LLMs can significantly enhance actuaries’ analytical capabilities and improve risk management and business outcomes while simultaneously reducing error and improving efficiency.
Overall, this paper provides a technical introduction and demonstration of the applications of LLMs in insurance and actuarial work, highlighting the potential benefits of using these models to analyse and interpret information and to improve risk management and business outcomes.
The paper is structured as follows: Section 2 discusses direct applications of LLMs to insurance and actuarial work, where the LLM is embedded within the process programmatically. This section considers the claims management process in detail, followed by less detailed discussions on other areas of application. Section 3 then discusses indirect applications of LLMs to insurance and actuarial work, where the LLM is used as an assistant to aid the actuary in their work. As LLMs are a new technology, Section 4 provides a primer on LLMs, explaining how to access and use them, including some common approaches such as prompting and few-shot learning. A number of case studies are presented in Section 5 to demonstrate the practicality of using LLMs in actuarial work. Section 6 discusses the impact of LLMs on actuarial work, as well as ethical and professional considerations at a high level. Finally, Section 7 concludes the paper.
2. Direct Applications of LLMs in Actuarial Work
LLMs can be used directly within actuarial work or as an aid to help actuaries complete their work. “Direct” applications involve using the LLM within the actuarial process, while “indirect” or “assistance” applications involve using the LLM to help actuaries complete their work.
An example of a direct application of LLMs in actuarial work is the use of LLMs to categorise claims based on free-text claims descriptions. In this case, the LLM is a distinct step in the actuarial process. An example of an assistance application of LLMs would be an actuary using an LLM to draft a summary of a reserving report capturing the key points in the document.
In this section, the use of LLMs directly as a distinct item in the actuarial process is discussed. Section 3 considers the applications of LLMs as an assistant. The discussion begins by considering the end-to-end claims process and identifying how LLMs can be directly used at each point. Thereafter, other areas are considered at a higher level, providing ideas on how LLMs can be useful, but without detailing their application at each step of the process.
2.1. Detailed Applications of LLMs in the Claims Process
Claims processing involves several steps where LLMs can be beneficial. The typical claims process is shown in Table 1. It is important to note that the process and examples below are mostly specific to a non-life motor insurance claim. This process is selected as it is one of the most important functions undertaken by insurers. Further, it should be easily digestible by a wide range of actuarial and insurance audiences. The process itself also contains several areas requiring communication in various forms and collection of both structured and non-structured data. This makes it an ideal candidate for the demonstration of the application of LLMs. Other functions within insurance can also benefit from the use of LLMs but are not discussed in as great detail in this paper.
The following sections in Table 1 expand on each item and discuss how an LLM can possibly be used. For a more detailed exploration, see Dimri et al. (Reference Dimri, Paul, Girish, Lee, Afra and Jakubowski2022).
Table 1. Claims management process
2.1.1. Reporting
At the reporting stage, insurers can collect specific free-text fields from interactions with policyholders. For example, the transcript of a phone call or the body of an email. Insurers can use an LLM to extract information from these free-text fields, such as the date of the incident, the location of the incident, the type of incident, and any other relevant information. This information can be used to automatically populate the claims report and database, reducing the workload on claims assessors.
One might argue that a well-built digital portal, such as a website or mobile application, can capture this information in a more structured format. This is true only for information that is anticipated and needed for all claims, such as the date of the incident, location, type, etc. However, in many cases, extra information is provided that is not anticipated or easily structured.
For example, a policyholder might describe the incident in their own words, provide additional context or details, or express their emotions related to the incident. This unstructured information can provide valuable insights into the claim, such as the severity or the potential for fraud. Such information can only be captured in a free-text input box in a digital portal. However, this information still needs to be extracted and processed by a claims adjuster; in this context, LLMs can be used to extract any additional valuable information.
Variants of LLMs can be used for specific tasks. Entity recognition models, trained to extract people, places, events, or other pertinent information from text, can be used to extract information from additional free text accompanying the reported claim. Similarly, sentiment analysis models can be used to extract the sentiment or emotions of the policyholder related to the incident.
This is not only useful for providing context to the claim but can also assist in detecting fraud or misrepresentation, whether intentional or not. For example, a policyholder might be more likely to exaggerate the severity of the incident if they are angry or upset. Further, sentiment or emotional recognition models can aid insurers in providing outreach services to policyholders who may need counselling or other support services.
LLMs can also summarise free-text fields, extract themes, or be allowed to freely adapt and extract any information deemed pertinent in a structured format. When paired with a NoSQL database, each claims report is considered a document within a collection of documents related to the claim and the policyholder. NoSQL databases are particularly well-suited for this use case due to their flexibility in handling diverse and unstructured data, which is common in claims processing. They can easily accommodate varying fields and data types, making them ideal for storing claims data that may not fit neatly into a fixed schema. Additionally, NoSQL databases can efficiently handle large volumes of data and integrate seamlessly with LLMs and other machine learning models. While SQL databases are still viable options for storing structured claims data, NoSQL databases offer greater adaptability for the specific needs of claims processing.
Additional benefits of LLMs include the categorisation of claims by peril. An LLM can use a claim description to determine if a fire occurred at a property or if motor damage occurred without a third party. This can be used to automatically categorise the claim and route it to the appropriate team. It can also be used to automatically categorise the claim for reporting purposes, such as to the relevant government agency, if any.
Structured collection of information, such as summaries of claims, sentiments, emotions, entities, etc., extracted by the LLM and stored in a flexible NoSQL database, can be built up over time and cross-referenced in the future. One purpose may be to identify errors, omissions, or inconsistencies in the claims process. Another purpose may be to identify patterns in policyholder behaviour that may indicate fraud. This can be combined with observing patterns across numerous policyholders to identify fraudulent networks of policyholders that report similarly or at similar times, with similar details.
Finally, more direct applications of LLMs are certainly viable too, for example, as the back end of a chatbot or chat interface.
All of the above provide significant efficiency and value to the claims process by automating laborious tasks, reducing the workload on staff, and providing valuable insights into the claims process that typically would cost too much time, money, or human resources to collect. All of this information can benefit the rest of the claims process as well.
2.1.2. Adjusting
Following the reporting stage, insurers may assign a claims adjuster to investigate the claim and gather relevant information. The claims adjuster will need to collect additional information. After the reporting stage, a wealth of information may already have been collected by any LLMs embedded in the claims reporting process. Another LLM could be used to summarise all this information into a structured report for the adjuster to review.
After review, the adjuster may need to collect additional information from the policyholder, any witnesses, and any third parties involved in the incident. Again, this may involve transcripts of phone calls, emails, or other free text that can be mined again by an LLM to extract more information that can be stored in the flexible NoSQL database alongside the other policyholder’s documents relating to the claim.
The adjuster may receive documents such as medical reports, police reports, witness statements, and other information related to the claim. These documents can be scanned and converted to text that can be input into an LLM. The LLM could summarise documents or extract important information as described in the reporting section. However, for certain classes of business, similar documents may be received frequently. For example, claims for medical expenses are often accompanied by medico-legal reports, and vehicle accidents are often accompanied by police reports. These reports are often similar in structure and content and can be used to fine-tune an LLM to extract and parse information from these reports in a more reliable manner. This may be simply for recording information or to identify policy violations, such as speeding or drunk driving in the case of motor-related claims.
An LLM can also be used to classify claims based on the information collected as low, medium, or high severity. This can be used to prioritise claims for investigation or mark certain claims as likely to be above a certain monetary threshold. For example, a claim description may describe a low-impact “bumper bashing” with minimal damage. In this case, the claim may be marked as low severity, and the adjuster may not need to investigate further. However, if the claim description describes a high-impact collision with significant damage and a lengthy description of the event, the claim may be marked as high severity, and the adjuster knows to investigate further.
2.1.3. Investigating
Based on the information provided, a claim may need to be investigated for fraud. Selecting claims for fraud investigation is a non-trivial problem for insurers, as it involves weighing the cost of additional investigation (in time, money, effort, and brand) against the probability that a claim is, in fact, fraudulent. Insurers invest significant resources in fraud investigation, and the cost of fraud is substantial. The extra information provided by LLMs can offer additional dimensions on which to assess whether a claim should be investigated for fraud. An LLM can be fine-tuned to identify signals in claims descriptions, figures provided, facts provided, etc., to provide additional context to the adjuster and help them make a decision on whether to investigate further based on all information provided.
For both adjusting and investigating, the LLM can be provided with the policy terms and conditions and the policy schedule to allow the LLM to work within the bounds set by the insurer.
Case Study 1 in Section 5 provides an example of using an LLM to find inconsistencies in claims documents.
2.1.4. Negotiating, agreements, and payments
Following reporting, adjusting, and investigation, the final parts involve more human interaction, and the direct inclusion of LLMs is less likely to be required. However, the benefits provided in earlier stages can be used to inform the negotiation process.
The insights gained from LLMs in the earlier stages can provide valuable context and background information for claims adjusters during the negotiation process. This can help in making more informed decisions, ensuring fair settlements, and potentially speeding up the resolution of claims. Additionally, the structured data collected by LLMs can be used to automate certain aspects of the payment process, further enhancing efficiency.
2.1.5. Compliance
Across all the steps above, insurers have a responsibility to comply with regulatory requirements. Often, these regulatory requirements are contained in verbose documents and are frequently changing. Reviewing processes in line with these requirements is challenging and time-consuming. An LLM can ingest the regulatory requirements as context and answer a given set of questions related to the claims documents to ensure regulatory compliance. Again, the benefit of having a NoSQL database with various documents collected per claim per policyholder allows this information to be flexible and easily processed.
Case Study 3 in Section 5 demonstrates how an LLM can be used to aid in regulatory compliance.
2.1.6. Other
Other benefits may also arise, and the applications above are not exhaustive. A key example is the translation of documents or claims information. This is pertinent in multilingual countries where claims information can be automatically translated into the primary language used by the insurer.
A meta-analysis can be performed on the overall output of claims processes to identify trends over time. The outputs shown above can be fed into an LLM to identify overall themes observed in the company over time. For example, the LLM can be instructed to identify themes of fraud and summarise them. This can be used to identify trends in fraud over time and pinpoint areas of the business that may need improvement to reduce fraud.
Additionally, LLMs can be used to enhance customer service in the claims process. By automating tasks such as document translation and trend analysis, insurers can provide faster and more accurate responses to policyholders, improving the overall customer experience.
2.2. High-Level Consideration of Other Insurance Functions
In this section, the discussion focuses on how LLMs can be integrated into various other functions beyond the claims process at a high level. Instead of delving into the specifics of each function, the focus will be on broad themes that highlight the diverse applications of LLMs in the insurance industry. These themes include emerging risks, underwriting, compliance, and more.
2.2.1. Identifying and managing emerging risks
Prudent risk management necessitates keeping abreast of risks across all sources, not only insurance and market risk. This often involves laborious consideration of various information sources, such as news outlets, regular reports, expert opinions, etc. Insurers may have individuals who collate this information and report on it at risk committee meetings. However, this is a time-consuming process that may not be comprehensive and is likely irregular and/or infrequent.
Case Study 2 demonstrates how scraped news results based on a list of focused search terms can be analysed using an LLM to identify emerging risks and even produce a high-level summary for a reporting pack. This can be used to inform risk management decisions, identify new emerging risks, and even identify new opportunities. The example is simplified, but the same approach can be used to analyse a wide range of information sources, such as social media, economic reports, and expert opinions on a much broader scale, using a robust framework.
The approach can even be used to determine trends over time. Prior results can be fed in as context to the LLM, and it can compare and contrast results. Company data can be included to identify risks specific to the insurer.
2.2.2. Commercial risk underwriting
Commercial underwriting often involves the consideration of technical reports, such as engineering reports, safety reports, and other technical documents. These documents are often lengthy and contain a lot of information that is not relevant to the underwriting process. LLMs can be used to ingest these documents and extract the relevant information for the underwriting process. This can streamline the underwriting process and reduce the time taken to underwrite a risk.
Appropriate prompt design can even specify the format of the results. For example, the LLM can be instructed to extract the key information from the report and present it in a JavaScript Object Notation (JSON) format. This can be used to automatically populate the underwriting system or even to populate a risk register.
Insurers can assess a larger volume of information when considering whether to accept a risk or the implications of accepting a risk, including the indirect impacts of accepting a risk. This is pertinent in the current environment where transition risks related to climate change are becoming more prevalent, and insurers are looking to understand the impacts of these risks on their portfolios. Insurers can collect information on their insured risks to process their financial and climate disclosures, as well as news reports and analyses, to understand their exposure to transition and physical climate risks.
Further, the power of collective action by the public, fuelled by social media, can impact commercial policyholders and, by extension, the insurers that cover them. Insurers can use LLMs to ingest and analyse social media posts, news, websites, or other publicly available information to identify trends in customer sentiment and identify potential risks to the business. This can be used to inform pricing decisions or even to identify potential risks to the business that may not be covered by the policy.
2.2.3. Compliance
Insurers are subject to a wide range of regulations and are required to continuously comply with regularly changing regulations. This can be a time-consuming process, often performed manually, with scope for human error. However, the large volumes of regulations and the regular changes to them are difficult for LLMs to handle. LLMs are infrequently updated due to the large volumes of data required to train them, and the time and cost involved in training them. Further, LLMs are typically built with a limited context length, meaning they can only consider a few thousand words at a time. One approach to bypassing this limitation is given in Section 5.3.
This approach can be leveraged for numerous uses. For one, the insurer can form an internal database of their company-specific documents and LLMs to operate within the context of the insurance company. Additionally, the insurer can form a database of their help documentation and allow policyholders to interact with a chatbot to find information based solely on the insurer’s documentation. This can be used to reduce the number of queries to the call centre and improve customer service. In an advanced state, policyholder-specific knowledgebases can be formed where the LLM’s context can be specific only to the data of the policyholder. This can be used to provide policyholders with a more personalised experience. For example, a policyholder can query: “What is my excess and when was the last time I claimed?” and the LLM can respond with the excess applicable to the policyholder and their claim history.
The above demonstrates just one application of LLMs in compliance. Another application may be to ask the LLM to directly assess an internal document against regulatory requirements. For example, the insurer may feed their Own Risk and Solvency Assessment Policy into the LLM and request the LLM to perform a gap assessment and review of the policy against the requirements of the Governance and Operational Standards of Insurers (GOI).
2.3. How to Identify Direct Applications
This paper has demonstrated some applications of LLMs in the insurance domain. However, to aid readers in identifying direct applications of LLMs in their own organisations, a framework has been developed to determine whether an LLM could be used. The framework consists of two decision trees:
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1. Technical assessment tree: It is important to first understand whether an LLM is an appropriate tool for the task at hand. The Technical Assessment Tree serves this purpose. It guides the evaluation of the nature of the data in question, the complexity of the task, the potential benefits against the costs, and the practicalities of implementation. This tree helps to decide if an LLM is technically suitable for the task at hand.
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2. Risk assessment tree: Once the Technical Assessment Tree indicates a potential fit, it’s essential to understand the broader implications of deploying an LLM. The Risk Assessment Tree is designed for this phase. This tree ensures that while an LLM might be technically suitable, it doesn’t introduce unacceptable risks or overlook critical considerations.
2.3.1. Technical assessment tree
The Technical Assessment Tree is designed to assess whether an LLM is technically suitable for the task at hand. It is important to note that the Technical Assessment Tree is not designed to assess whether an LLM is the best tool for the task at hand, but rather whether it is technically suitable. The Technical Assessment Tree is shown in Figure 1.
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1. Is the data structured or primarily numeric in nature?
Figure 1. Technical assessment tree.
The initial consideration is the nature of the data. If the data is primarily structured or numeric and requires precise calculations, traditional statistical or algorithmic approaches might be more appropriate. For instance, tabular claims data without dynamic text fields might not necessitate the capabilities of an LLM.
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2a. Does the task require generating new content based on input?
The task might involve text data that needs processing, but if it’s not fundamentally generative or doesn’t require additional context, other forms of language processing might be more suitable. For example, translating text from one language to another is a language processing task, but it’s not generative. Thus, a traditional machine learning approach might be more fitting. Sentiment analysis is another example. Existing language models trained for sentiment analysis might outperform LLMs. However, if the task is fundamentally generative, such as generating a document summary or understanding text in the context of other text, an LLM might be more apt. If the goal is to generate additional data from the input, an LLM is likely the right tool for the task.
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2b. Does the task require context and complex pattern recognition?
More complex tasks may be suitable for LLMs. Specifically, does the task require a deep understanding of context and the ability to recognise intricate patterns? LLMs, designed to understand and generate human-like text, excel in situations where context and pattern intricacies are crucial. If a task demands such capabilities, an LLM may be appropriate. For example, identifying specific structures within large, complex documents would benefit from an LLM. However, extracting information from a table or a simple, highly standardised, and structured document may not require an LLM’s capabilities.
It’s common to consider applying LLMs only to unstructured data. However, the focus should be on how LLMs can impose structure on data, rather than how the data itself is structured. Nearly all the case studies demonstrate this. For instance, in Case Study 1, the input data is mostly structured with a clear schema, but the LLM’s role is to further structure it by parsing the claims descriptions. Case Study 2 involves taking unstructured input data in the form of multiple articles and structuring it into a list of themes with a summary. Case Study 4 involves extracting structure from documents, finding key elements of reinsurance slips, and forming them into a defined JSON structure.
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3. Does the benefit of using an LLM (accuracy, efficiency, automation) outweigh the costs (running, implementation, maintenance)?
While LLMs can offer superior accuracy, efficiency, and automation, they come with associated costs, including monetary expenses, computational demands, and maintenance overheads. If the benefits don’t justify these costs, more pragmatic or even manual solutions might be preferable.
Typically, an LLM is included to either increase the speed of a process through automation or to improve the quality of a process through enhanced accuracy and reduced error. In the case of automation, one typically assesses the development cost and time against the proposed savings. This requires considering the frequency and duration of a process. Infrequent, quick processes are not candidates for automation, as the cost of development and maintenance is unlikely to be offset by time saved.
The benefits are harder to quantify when the LLM is used to improve process quality. In this case, the actuary must consider the cost of errors and the potential reduction in errors that the LLM can provide. For example, in a compliance process, human error or oversight can be costly if certain compliance requirements are not met. An LLM can reduce the risk of human error and thus the risk of non-compliance. The actuary must consider the cost of non-compliance and the potential reduction in this cost that the LLM can provide.
Importantly, one must also consider the cost of errors made by the LLM itself. This is considered in the Risk Assessment Tree.
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4. Do you have the necessary resources (data, computational power, expertise) to implement and maintain an LLM solution?
Finally, the practicalities of implementation are considered. Deploying and maintaining an LLM requires specific resources, including data, computational power, and expertise. Without these, even if an LLM is technically suitable, its deployment might not be feasible. It’s essential to either have the necessary expertise in-house or be prepared to seek it externally.
This also involves building any supporting processes, such as the vector embedding database seen in Case Study 3. Further, like all models, LLMs need to be maintained and monitored. Performance should be continuously monitored and adjustments made as necessary to optimise the solution. If possible, objective metrics should be used to measure performance. However, due to the generative nature, performance assessment is often subjective and relative to the user’s requirements.
2.3.2. Risk assessment tree
The Technical Assessment Tree assesses the technical suitability of an LLM for a given task. It is important to note that the Technical Assessment Tree does not evaluate whether an LLM is the best tool for the task at hand, but rather whether it is technically suitable. The Risk Assessment Tree is shown in Figure 2.
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1. Could inherent bias in LLMs negatively impact the task?
Figure 2. Risk assessment tree.
LLMs, trained on massive amounts of internet data, are subject to inherent biases from the training data or the model’s information processing. It is necessary to evaluate whether these biases could negatively impact the task at hand. The information generated by the LLM should also be free from bias to prevent influencing those who use the results. If biases are possible, it is important to consider whether these biases can be mitigated.
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2. Are there any ethical considerations?
Beyond biases, ethical considerations may arise in the use of LLMs. A notable example is using an LLM to process social media data. It is necessary to consider whether the use of an LLM in this context is ethical and how the public would interpret such uses. Any ethical concerns should be addressed before proceeding.
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3. Is interpretability essential for the application?
Actuaries need to attest to the results of their processes, so interpreting and understanding how a model arrived at a result is often critical. If interpretability is deemed essential, it is necessary to determine how, whether by design or through additional processing, the results of the LLM can be explained. If not, then alternative models must be sought.
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4. Is there a risk of data leakage or unintentional memorisation?
Depending on how the LLM is accessed, concerns about data leakage to third-party providers may arise. This can lead to contraventions of privacy laws, brand damage, and legal action. Further, the LLM may unintentionally memorise data it has received, which could then be shared accidentally with other areas or external processes. This is particularly important for LLMs that may have external interfaces. Appropriate privacy management practices and layers of defence should be implemented to mitigate these risks.
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5. Is some variance in results acceptable?
Due to their generative nature, LLMs can exhibit variability. This means that for slightly varied or even identical inputs, the outputs might differ, depending on the initial prompt. This variability can be acceptable or problematic, depending on the application. Some LLMs provide parameters to manage variability. It is necessary to ensure that the range of variation in results is adequate for the task at hand.
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6. Are adversarial conditions likely and will the data vary considerably over time?
Slightly altered inputs can greatly influence the result of an LLM’s output. This could stem from differences in input data, or nefarious actors could alter their data to influence the LLM’s output. This is more pertinent for LLMs with an external interface. Additionally, it is necessary to consider how much the data may vary and how this will impact the output of the LLM. If these risks cannot be handled appropriately through the design or other interventions, an alternative model may be better suited.
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7. Are the frequency and severity of implications of model errors or failures acceptable?
Finally, it is necessary to consider the consequences when the LLM makes an error or fails. This should consider not only the possible frequency of failure but more importantly the severity. Errors may be inconsequential, such as a spelling mistake, or larger, such as a misinterpretation of a compliance requirement or generating misinformation that influences a critical strategic decision. It is necessary to consider the frequency and severity of errors and whether these are acceptable and apply robust risk management techniques and oversight to LLMs.
2.4. Example Application of Decision Framework
In this section, the decision framework is applied to the task of extracting structured information from reinsurance treaties. This same task is presented in Section 5.4.
Ideally, a system would be desired that can take the contents of a reinsurance treaty in an unstructured text format as input and produce a structured output with key elements clearly identified, such as the lead reinsurer, the ceding commission, etc. This would enable automation of the process of extracting information from reinsurance treaties, which is currently a manual process, saving time and reducing the risk of human error.
First, the technical assessment is considered in Table 2.
Table 2. Example Technical Assessment for Extracting Data from Reinsurance Treaties
LLM, large language model; API, Application Programming Interface.
The technical assessment identifies that an LLM is well-suited to the task and can be implemented with minimal difficulty. The risk assessment is now considered in Table 3.
Table 3. Example Risk Assessment for Extracting Data from Reinsurance Treaties
LLM, large language model.
The risk assessment identifies minimal risk in using an LLM for this task. Implementation of the LLM can therefore proceed. Note that the example above is simplified, and within the technical and risk assessment processes, some testing and development work may be conducted, especially for more complex tasks. However, the example above demonstrates the process of assessing the suitability of an LLM for a task.
3. Assistance Applications of LLMs in Actuarial Work
LLMs can also be used for indirect assistance with actuarial work, for example, as an assistant with whom a conversation can be had to help complete tasks or a partner to challenge ideas and thinking. In this section, examples are provided of how LLMs can be used as an assistant in actuarial work. These are not exhaustive, and the use of LLMs as conversational assistants is as broad as the imagination allows.
3.1. Coding Assistant
In Appendix C, excerpts of a conversation are provided with ChatGPT on using Python to perform incurred but not reported (IBNR) reserving. This demonstrates engaging with ChatGPT as an assistant to help an actuary write code. The responses have not been edited, nor have the results been checked for accuracy. In the experience of the author, the code generated by ChatGPT is generally of good quality. However, ChatGPT does at times write code that is incorrect or uses methods or packages that do not exist.
LLMs can also be used to help debug code. For example, if an actuary is struggling to understand why a piece of code is not working, they can engage with an LLM to help them understand the issue. This can be particularly useful when working with complex code, or code that has been written by someone else. Further, the actuary can also engage with LLMs to assist with code optimisation, best practice, style, and general guidance.
There are also LLMs specifically designed for coding assistance, such as GitHub’s Copilot Footnote 1 . These models are trained on a diverse range of public code repositories, which enables them to provide suggestions for a wide variety of programming languages and tasks. While these models can be a valuable tool for generating code, they share the same limitations as ChatGPT in terms of the need for careful review and potential optimisation of the generated code.
3.2. Problem-Solving
LLMs can also be used as a tool for problem-solving in actuarial work. By providing a natural language interface, LLMs can help actuaries articulate and refine their problems, explore different approaches, and generate potential solutions. This can be particularly useful in complex or novel situations where traditional methods may not be applicable or effective.
For example, with appropriate prompts, a solution to a problem can be designed by engaging with the LLM in a conversation. The LLM can ask questions to clarify the problem and provide suggestions for potential solutions. This can be a valuable tool for exploring different approaches and generating new ideas. An actuary can also pose solutions to the LLM, and the LLM can provide feedback and suggestions for improvement. This can be a valuable tool for refining and optimising solutions.
LLMs can be particularly beneficial when actuaries face challenges that require a multidisciplinary approach. For instance, when actuaries are dealing with emerging risks, such as climate change or pandemics, they might need insights from fields like environmental science or epidemiology. LLMs can bridge this knowledge gap by providing relevant information from these domains, thereby aiding actuaries in developing more holistic risk models.
3.3. Drafting Reports and Summarisation
LLMs can assist in drafting reports and summarising complex information. One might consider providing an LLM with raw data or preliminary analysis and letting the LLM generate a draft report; however, this is insufficient guidance. Rather, it is important for an actuary to provide the LLM with sufficient guidance and context; otherwise, the results will be of poor quality. For example, an actuary may provide the LLM with a draft report and ask the LLM to generate an executive summary of the report. Further, the actuary should specify the main sections of the summary, the tone, and any areas to focus on. LLMs should be treated as an assistant rather than the primary author of a report.
LLMs can also assist with reviewing text, improving grammar, conciseness, etc. One can even prompt the LLM to take the role of the audience of the report and advise on how well it was received and on what additional information it may have preferred to see.
In addition to drafting reports, LLMs can be used to translate complex actuarial findings into layman’s terms. This can be particularly useful when communicating with stakeholders who might not have a deep understanding of actuarial science. By providing a clear and concise summary, actuaries can ensure that their findings are understood and actionable.
Moreover, LLMs can be prompted to generate visual representations, such as charts or infographics, to accompany the textual content, making the reports more engaging and easier to digest.
3.4. Education
One criticism of LLMs is that they may lead users to become dependent on them to do their work. For example, one might rely on the LLM to generate code or content, without engaging with and learning from the results. However, LLMs can be used as teachers and are extremely effective in this regard. For example, an actuary could use an LLM to learn a new statistical method or programming language. The model could provide explanations, examples, and even interactive exercises to help an actuary understand and apply the new concept. This can be a valuable supplement to traditional learning resources, particularly for self-directed learning. An actuary can ask specific direct questions and receive responses in their preferred language and at their chosen complexity. One can even request the LLM to use analogies or creative explanations to help with understanding.
LLMs can also be used to simulate real-world scenarios for training purposes. For instance, an actuary can interact with the LLM to simulate a business scenario where they need to assess the impact of a new regulation or a market event. The LLM can provide real-time feedback, helping the actuary refine their approach.
3.5. Data Cleaning and Preparation
ChatGPT plugins Footnote 2 such as Code Interpreter allow users to upload data to ChatGPT, along with a description of what task they would like to perform. ChatGPT will then generate Python code, run the code, and present the results to the user.
This greatly speeds up the data exploration and preparation stage of any data project. Further, users can ask ChatGPT to write code to perform cleaning tasks, modelling tasks, etc., which they can then export and use locally.
Beyond just cleaning and preparing data, LLMs can be instrumental in identifying anomalies or inconsistencies in the data. They can be prompted to run exploratory data analysis to provide insights into the distribution, correlations, and potential outliers in the dataset.
3.6. Model Development and Interpretation
An actuary can use an LLM to assist in understanding, developing, and interpreting models. For example, Appendix C.2 provides an excerpt on using ChatGPT to understand an unfamiliar model and assist with hyper-parameter tuning.
LLMs can also assist in model validation. Once a model has been developed, an actuary can discuss the model’s assumptions, structure, and results with the LLM. The LLM can then provide feedback on potential areas of improvement or highlight any assumptions that might not hold in real-world scenarios.
Furthermore, LLMs can be used to explain complex models to non-technical stakeholders. For instance, if an actuary has developed a machine learning model to predict insurance claims, the LLM can help translate the model’s findings into actionable business insights.
3.7. Other Applications
There are many other potential applications of LLMs in actuarial work. For example, LLMs could be used to:
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Automate routine correspondence, such as generating basic summaries of regularly downloaded data.
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Generate documentation for code or workbooks.
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Perform scenario testing and stress testing. Given their vast knowledgebase, they can simulate various economic, financial, or environmental scenarios to assess the potential impact on insurance portfolios. See Appendix C.
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Monitor regulatory changes. Relevant updates can be sent to actuaries, along with summaries. This can reduce the risk of regulatory breaches and penalties.
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Assist with research and knowledge management. LLMs can help actuaries scan documents to find pertinent information. They can also summarise lengthy documents and point actuaries to areas they should review in detail.
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Aid in training and onboarding. LLMs can ingest company information and processes and assist new joiners with onboarding and getting familiar with the process.
The key is to identify tasks where the capabilities of LLMs can complement the skills and expertise of the actuary and to use the models in a way that is ethical, responsible, and aligned with professional standards.
4. Primer on How to Use LLMs
Before demonstrating case studies, this section begins with a primer on how to use LLMs in various capacities, as well as some additional information on prompting, few-shot learning, and context length.
4.1. Accessing LLMs
There are several ways to use LLMs in actuarial work. This section details four approaches in order of increasing complexity.
4.1.1. LLMs through the browser
Most actuaries unfamiliar with LLMs would have been exposed to their first LLM through the popular ChatGPT website. ChatGPT is based on the GPT-3.5 and GPT-4 LLMs. ChatGPT is fine-tuned for conversational English and is able to generate human-like responses to user input. The website allows users to interact with ChatGPT through a simple interface, where they can type in a prompt and ChatGPT will generate a response.
Despite being focused on conversational English, ChatGPT has detailed knowledge of a wide range of topics, including actuarial science. This makes it a useful tool for actuaries who want to explore the capabilities of LLMs without having to learn how to use them.
Using ChatGPT through the browser is most suited to assistance applications as the user has to manually discuss and fine-tune answers to prompts given. For more advanced use cases such as those detailed in Section 2, the process would be far too cumbersome to be beneficial. For example, to summarise claims descriptions or documents, the actuary would need to manually copy and paste the claims description into the ChatGPT website and copy and paste the response out, along with the associated prompts. This would be far too time-consuming to be useful for anything more than a few tens of claims.
Instead, the ChatGPT website is more useful as a productivity aid as described in Section 3.
One also needs to consider the privacy and security implications of using the website. The ChatGPT privacy policy indicates that it collects personal information based on the users’ use of the service. This means that the actuary must be careful not to share private and confidential information as this will be collected by OpenAI. This almost certainly excludes ChatGPT from being used in any direct or indirect applications that involve personal information, confidential information, or company information. In fact, several companies have opted to ban the use of ChatGPT outright, going as far as to block access to the website Footnote 3 . Further discussion on privacy and ethics is given in Section 6.
Alternatives to ChatGPT exist, such as the Bing Chat functionality Footnote 4 or Google’s Bard Footnote 5 . However, these alternatives are not as widely adopted or as advanced as ChatGPT in multiple respects at the time of writing.
4.1.2. OpenAI API and other APIs
OpenAI, the company that provides the ChatGPT website, provides an Application Programming Interface (API) that allows the actuary to programmatically access several LLMs, including the LLM behind ChatGPT. Several model variants exist, including models fine-tuned for tasks such as code explanation, summarisation, or question and answering. The service is provided at a cost based on the number of tokens in the prompts, which is loosely linked to the length of the prompts provided and responses generated. This approach is used in all of the case studies in this section. Despite the API approach being automated, the same privacy and security concerns as the ChatGPT website apply.
In addition to programmatic access to LLMs, the OpenAI API provides additional flexibility in the form of several different models as well as model-specific parameters. These parameters allow the actuary greater control over the response. For example, the temperature of the response, which defines how creative the LLM can be, can be varied. The lower the temperature parameter, the more deterministic the response, and the more focused the query. A higher temperature allows the LLM to be more varied in its response. In most analytical applications, it is likely that a lower temperature would be preferred, as the actuary would want the LLM to be focused on the query. However, in some cases, a higher temperature may be preferred, for example, if the actuary is looking for inspiration or ideas. One can also select a model more specifically suited to the task at hand, or with longer context lengths, or updated features.
Other APIs to LLMs are slowly emerging, such as GooseAI Footnote 6 and Cohere Footnote 7 . However, these APIs are not as advanced and do not have as many features as the OpenAI API at the time of writing. One can also access open-source models such as the OpenAssist Pythia 12B Footnote 8 model, Bloom (BigScience Workshop et al., 2023), or SantaCoder (Allal, Reference Allal, Li, Kocetkov, Mou, Akiki, Ferrandis, Muennighoff, Mishra, Gu, Dey, Umapathi, Anderson, Zi, Poirier, Schoelkopf, Troshin, Abulkhanov, Romero, Lappert, Toni, del Río, Liu, Bose, Bhattacharyya, Zhuo, Yu, Villegas, Zocca, Mangrulkar, Lansky, Nguyen, Contractor, Villa, Li, Bahdanau, Jernite, Hughes, Fried, Guha, de Vries and von Werra2023) for coding-specific tasks.
The case studies in this section demonstrate how to use the OpenAI API to access LLMs.
4.1.3. Local large language models
Users can opt to host a local version of open-source LLMs. This solves security and privacy concerns as the insurer will completely control how the data fed into the LLM is used and stored. However, this approach is not trivial and will require the Information Technolodgy (IT) function of the insurer to set up a server with sufficient Graphics Processing Unit (GPU) computing to run the LLM. The compute requirements of LLMs are significant depending on the model used. LLMs are large models due to the number of parameters they use, ranging from a few billion parameters, capable of fitting within a few gigabytes of video memory, to several hundred billion, requiring hundreds of gigabytes of video memory.
As such, running local LLMs is likely not realistic for all but the largest insurers with sufficient resources to do so. Over time, it is likely that LLM providers will offer controlled access to local LLMs, which will allow insurers to benefit from the privacy and security benefits of local LLMs without the compute requirements. However, this is not yet available at the time of writing.
4.1.4. Custom build
Finally, insurance companies can opt to build their own LLMs. This is a significant undertaking and is likely not feasible for all but the largest insurers. However, it does provide the most flexibility and control over the LLM and allows the insurer to build a model that is specifically suited to their needs. For example, the insurer can build a model that is specifically trained on their own data or a model that is specifically trained for a particular task. This approach is not discussed further in this paper as it is not feasible for most insurers at the time of writing.
4.2. Additional Information on LLMs
4.2.1. Context length and tokenization
Section 2.2.3 briefly touched on the limited context length of LLMs. This section expands slightly on that topic. LLMs are limited in the amount of context they can ingest and understand. Context is loosely translated to the number of words the LLM can keep in its memory at a time. Appendix B provides a more detailed list of definitions for those unfamiliar with these terms.
Originally, GPT-3.5 had a context length of about 4,000 tokens, which roughly translates to about 3,000 words of standard English text or 6 pages at 500 words per page. In recent revisions of GPT-3.5, this has increased to 16,000 tokens or about 12,000 words (24 pages). Footnote 9 This is still insufficient to ingest and understand the large volumes of regulations that insurers are subject to. For example, IFRS 17 Insurance Contracts is 102 pages Footnote 10 .
Typical approaches to handling this problem are training models with increased context length or using what is known as a vector database. Refer to Section 5.3 where Case Study 3 utilises a vector database to store embeddings of the Financial Soundness Standards for Insurers (FSI) and the GOI, both of which are features of the South African Solvency Assessment and Management (SAM) framework.
4.2.2. Prompt engineering
Before exploring the case studies of this section, it is important to understand the concept of prompt engineering and few-shot learning. LLMs, especially chat-based LLMs, are general models that are trained on a wide variety of data. As such, they are not specifically trained for any particular task. Thus, it is important to engineer a prompt that focuses the LLM on the task at hand and then guides the LLM to provide the output required through additional prompts.
The prompt is a short piece of text that is provided to the LLM to indicate the task that the LLM should perform and how. Below is an example of a prompt termed the “Universal Critic” Footnote 11 , which is used to critique work within a specified domain.
Figure 3. Universal critic prompt.
One can observe that the prompt serves to focus the LLM on the specific task, dictating both the format and content of responses. Prompt engineering, a nascent field emerging with the popularisation of LLMs, remains an area of ongoing exploration and development, with likely evolution over time. Engaging an LLM without a carefully crafted prompt often yields suboptimal results compared to those achievable with a well-designed prompt. In working with LLMs, the process of crafting a solution frequently involves iterative prompt modification to attain the desired outcome. Prompts are exemplified in the code accompanying the case studies.
Prompt engineering is an active research area; for a comprehensive introduction, the reader is referred to White et al. (Reference White, Fu, Hays, Sandborn, Olea, Gilbert, Elnashar, Spencer-Smith and Schmidt2023).
4.2.3. Few-shot learning
In certain situations, particularly in conversational contexts, an LLM may not produce the desired output based solely on the prompt. In such instances, few-shot learning can be employed to guide the LLM towards the desired output. Few-shot learning is a technique in which the LLM is presented with several examples of the desired output and then tasked with generating an answer consistent with those examples. The LLM can then “learn” how to structure its responses based on the provided examples. LLMs have been shown to perform well with few-shot learning (Brown et al., Reference Brown, Mann, Ryder, Subbiah, Kaplan, Dhariwal, Neelakantan, Shyam, Sastry, Askell, Agarwal, Herbert-Voss, Krueger, Henighan, Child, Ramesh, Ziegler, Wu, Winter, Hesse, Chen, Sigler, Litwin, Gray, Chess, Clark, Berner, McCandlish, Radford, Sutskever and Amodei2020). Few-shot learning is demonstrated in Appendix C.
5. Case Studies
This section presents several simplified case studies illustrating the application of LLMs in actuarial and insurance work. These case studies are not exhaustive and aim to serve as a foundation for actuaries considering the potential use of LLMs in their practice.
5.1. Case Study 1: Parsing Claims Descriptions
In this case study, GPT-4 was employed to parse interactions with policyholders during the claims process to assess the sentiment of the engagement, the emotional state of the claimant, and inconsistencies in the claims information to aid downstream fraud investigations. It is important to emphasise that the LLM functions as an automation tool in this context and is not intended to supplant human claims handlers or serve as the ultimate arbiter in fraud detection or further engagements. Instead, it aims to support claims handlers by analysing the information provided by the claimant, summarising the engagement, and offering a set of indicators to inform subsequent work.
The code for this case study can be found in the accompanying GitHub repository Footnote 12 . Written in Python, the code utilises the OpenAI API to interact with GPT-4. The claims information used in this study is fictitious, generated by an LLM for the purposes of this paper.
An example of claims interaction is provided below.
Figure 4. Claims data for example claim interaction in JSON format.
The following are examples of medical and police reports that accompany the claim.
Figure 5. Medical report for example claim interaction.
Figure 6. Police report for example claim interaction.
The following prompt is given to the LLM. The objective is to supply the LLM with the information provided by the claimant and request a summary of the claim, identification of any inconsistencies in the information, and an assessment of the emotional state of the claimant, along with the reasoning behind these determinations. Finally, the LLM is asked to indicate whether further assessment is necessary and, if so, why.
Figure 7. Prompt for Case Study 1.
The response is formatted in JSON for ease of integration with other systems. The raw response is provided below.
Figure 8. GPT-4 response for Case Study 1 in JSON format.
As observed, the LLM has extracted the claim number and policy number from the transcript. It has identified some inconsistencies in the claim. The LLM has also determined that the claimant is angry and has provided reasoning for this assessment. The LLM has summarised the claim and determined that further assessment is required, offering reasoning related to its understanding of the reports provided and the nature of the calls.
While the examples presented here and in the accompanying GitHub repository are illustrative, they serve to demonstrate the potential utility of LLMs. In practice, the prompt would be defined more carefully, and more information would be extracted regarding the LLM’s reasoning. In fact, it is likely that separate prompts would address each item (inconsistencies, emotion, further investigation).
5.2. Case Study 2: Identifying Emerging Risks
In this case study, GPT-4 is tasked with summarising a collection of news snippets to identify emerging cyber risks. The script conducts an automated custom Google Search for recent articles using a list of search terms. It extracts the metadata of the search results and employs GPT-4 to generate a detailed summary of the notable emerging cyber risks, themes, and trends identified.
Subsequently, GPT-4 is requested to produce a list of action points based on the summary. Each action point is then input into GPT-4 again to generate project plans for fulfilling the action points.
This case study and its associated code demonstrate, at a basic level, the ease with which LLMs can be integrated directly into actuarial and insurance work, including additional prompting against its own output to accomplish further tasks.
The resulting summary is provided on the following page.
Figure 9. GPT-4 summarisation of automatically collected Google searches relating to cyber risk.
The LLM has succinctly summarised the major themes in a digestible format for the Board. Subsequently, the LLM took its own summary and produced a list of action points for the Board to consider. The action points are presented below.
Figure 10. GPT-4 action points generated from GPT-4 summary.
Finally, GPT-4 took its own action points and generated project plans for each action point. The project plan for one of the action points is displayed below.
Figure 11. GPT-4 project plan generated from GPT-4 action point.
5.3. Case Study 3: Regulatory Knowledgebase
As outlined in Sections 2.2.3 and 4.2.1, LLMs have a limited context length and are thus unable to digest large volumes of regulatory documents. Case Study 3 employs a vector database to store embeddings of the FSI and the GOI, both of which are components of the SAM framework. Essentially, a regulatory knowledgebase is created, and any queries can be answered by the LLM by searching the knowledgebase for the most relevant document. This innovative approach can streamline the compliance process and reduce the time required for compliance assessment.
Utilising the “OP” stack (where “stack” refers to the layers of technology used to build a software process), named for its use of the OpenAI API and the Pinecone Footnote 13 database, the knowledgebase is constructed as follows: Regulatory documents are fed into the OpenAI embedding endpoint to generate vector embeddings of the documents. These embeddings are then stored in the Pinecone database, a vector database that enables fast vector searches. Additionally, a website is developed that allows users to input a text query, which is then used to search the vector database. The vector embedding of the text query is compared to the vector embeddings of the regulatory documents, and the most similar documents are returned to the user. These documents serve as context for the LLM to generate a response to the user’s query. This approach enables the LLM to answer questions within the context of the provided regulatory documents, resulting in more accurate and relevant responses.
For example, consider the following prompt and response.
Figure 12. Real estate investment prompt to ChatGPT.
Figure 13. Real estate investment prompt to ChatGPT supported by the regulatory knowledgebase.
Figure 14. Broker acquisition prompt in the context of SAM to ChatGPT.
Figure 15. Broker acquisition prompt in the context of SAM to ChatGPT.
Although not a poor answer, it is not specific to the domain of the question. When the same question is posed to the LLM with the regulatory knowledgebase, the following response is generated.
Figure 16. Broker acquisition prompt to ChatGPT supported by regulatory knowledgebase.
This answer is much more targeted, and the context used was provided by the LLM in a separate response. This demonstrates that the LLM can use the regulatory knowledgebase to provide more accurate and relevant responses to questions.
One might argue that a more appropriate prompt should be used to prime ChatGPT to provide a more relevant response. This is an alternative approach that can be employed, but it requires the user to construct a prompt that will elicit the desired response, which is not always straightforward. Moreover, the responses can still be generic and not specific to the domain of the question, especially for very specific requirements. The regulatory knowledgebase approach allows the user to input a question and receive a response that is specific to the domain of the question, without having to construct a prompt. Below, slightly more context is provided to ChatGPT to see if it can produce a more relevant response.
Although improved, the response is still not specific to the domain of the question. When the same question is posed to the LLM supported by the regulatory knowledgebase (excluding the initial prompt to be domain specific), the following response is generated:
The response, although not providing a definitive answer, is much more specific to the domain of the question. It provides references to the relevant sections of the SAM framework, which can be used to obtain more information. It should be noted that these results were obtained using a very basic approach to building a knowledgebase with no additional tuning or optimisation. The results can be improved by using a more sophisticated approach to building the knowledgebase and paying specific attention to the construction of the database itself. Furthermore, it should be noted that the vector database does not fundamentally change the LLM. It simply provides a way to incorporate additional semantic information into the model’s inputs or outputs to enhance its results.
Accompanying code is not provided, as the full code is verbose, and not all of it is relevant to this paper (particularly the detailed API calls and front-end JavaScript code). However, creating such a knowledgebase has become significantly easier thanks to open-source contributions. The reader is directed to the Vault-AI Footnote 14 GitHub repository, which provides a near-full solution to the problem. One needs only to upload the required documents and provide the necessary API keys.
5.4. Case Study 4: Parsing Reinsurance Documents
Reinsurance contracts are critical documents that are often lengthy and complex, making them time-consuming to analyse and understand. Moreover, they expose the insurer to significant risk if all contracts are not well understood. In this case study, LLMs are used to automate the process of extracting structured data from reinsurance contracts, such as the type of reinsurance, the reinsurer, the reinsured, the coverage period, and the premium amount.
This case study is accompanied by a Python script that employs an LLM to extract information from reinsurance contracts and convert it into a structured JSON format. The script uses the OpenAI API to interact with the LLM and the PyPDF2 library to extract text from PDF documents.
The processes of the script are:
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1. The text is extracted from the PDF document.
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2. A prompt is constructed using the extracted text and the question: “What is the JSON representation of this reinsurance contract?” and sent to the LLM along with a system prompt that instructs the LLM to return the JSON representation of the contract according to a given schema.
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3. The LLM returns the JSON representation of the contract, which is then saved to a JSON file.
The LLM is provided with the schema of the JSON format, which includes all the information typically found in a reinsurance contract. This enables the LLM to accurately extract and structure the contract information. It should be noted that the reinsurance contracts used in this study are simple, fictitious examples created for this paper that could certainly be processed directly without an LLM. However, in practice, the contracts would be more complex and varied, and significantly more information would need to be extracted, making an LLM a more suitable solution. Regardless of the complexity of the contracts, this case study also demonstrates how quickly one can leverage an LLM to perform a complex task with highly accurate results without having to build a complete programme.
Below is an example of the JSON representation of a reinsurance contract generated by the LLM. All original contracts and their generated JSON representations can be found in the GitHub repository accompanying this paper.
Figure 17. JSON representation of reinsurance treaty generated by GPT-3.5.
6. Impact on the Actuarial Profession and the Broader Insurance Industry
This section discusses the potential implications of using LLMs for actuarial work, such as their impact on the role of actuaries and the broader insurance industry. The primary goal of this paper is to demonstrate the potential of LLMs in the field of actuarial science and insurance and to encourage actuaries to explore the use of LLMs in their work. This paper does not attempt to provide a comprehensive analysis of the impact of LLMs on the actuarial profession and the insurance industry, nor does it consider in detail the risks, ethics, and professionalism issues associated with LLMs. However, a brief discussion of these topics is provided here to highlight their importance and encourage further research.
6.1. Impact on the Role of Actuaries
There is significant concern surrounding the use of AI and LLMs due to fears that they might replace jobs (Bock et al., Reference Bock, Wolter and Ferrell2020; McLeay et al., Reference McLeay, Osburg, Yoganathan and Patterson2021). Indeed, this may be the case in some industries where tasks are repetitive, easily automated, low-risk, and do not require significant human oversight (Bock et al., Reference Bock, Wolter and Ferrell2020; McLeay et al., Reference McLeay, Osburg, Yoganathan and Patterson2021; Vorobeva et al., Reference Vorobeva, El Fassi, Pinto, Hildebrand, Herter and Mattila2022). However, in actuarial work, it is challenging to identify a role that meets all these criteria. Moreover, considering the professionalism requirements of actuaries and the fiduciary duty insurers have to their policyholders, the author believes it is inadvisable to fully automate roles within the insurance sector. Regardless of the level of automation adopted, the regulation and professional duties and obligations will still lie with the individuals responsible for the process.
The author’s perspective is that LLMs will increasingly serve as tools to support actuaries in their endeavours. As illustrated in this paper, the primary benefits are enhancing efficiency, minimising human errors, and enabling actuaries to concentrate on tasks demanding general intelligence and reasoning, which hold significant value for insurers. For instance, an actuary who can save 10 hours by not having to extract information from documents can instead allocate those 10 hours to interpreting their work results, critically evaluating them, and integrating them within the organisation.
The widespread availability of LLMs and associated AI technologies will alter the way work is performed and cannot be ignored. Actuaries, as well as employers of actuaries, will need to ensure they are familiar with the technology and able to work with it. Not only will actuaries have to use, interpret, and understand the results of LLMs, but they will also need to manage the risks they pose. As LLMs and AI technologies become more integrated into actuarial work, actuaries will need to continuously adapt their skillsets and knowledge. This extends beyond simply using the technologies, but more importantly to understanding their limitations, potential biases, and ethical implications.
Actuaries will work in increasingly interdisciplinary teams, necessitating collaboration with professionals from other disciplines, such as data scientists, computer scientists, software engineers, and ethicists. Issues such as data privacy, algorithmic fairness, transparency, and the ethical use of AI will become even more important and impactful. Actuaries not only need to be aware of the changing regulatory landscape in response to AI but also advise and lead the way on appropriate regulation and legislation to manage the risks they pose.
Actuarial workflows may change significantly as a result of LLMs. Previously time-consuming and manual tasks may be automated away, leaving actuaries with time to focus on more complex tasks. This may result in actuaries spending more time on complex reasoning and problem-solving, or possibly even creativity. The use of LLMs in actuarial work may also have an impact on how actuaries communicate their findings and recommendations to stakeholders. Actuaries will need to ensure that they are able to effectively communicate the results of LLMs, as well as the limitations and uncertainties associated with these models.
6.2. Risks Associated with LLMs
Despite their often surprising and impressive performance, LLMs are not perfect. They are prone to errors, and these errors can be difficult to detect. This paper does not focus on these erroneous results not in an effort to conceal them, but rather because the focus of the paper is on applications and demonstrating them. However, it is important to note that during the course of our research, the LLMs utilised produced several errors that necessitated an iterative approach to improve results. In this regard, we have already reviewed and improved the results unless otherwise indicated (e.g. Appendix C). Presenting the entire process, including erroneous results and other errors to be described, would detract from the purpose of the paper. However, we do discuss some of the risks associated with LLMs here.
LLMs are only as good as the data they are trained on, which is typically large amounts of information gathered from the internet. Thus, LLMs are subject to the same biases and misinformation that humans who produce and consume internet content are exposed to. Hence, actuaries must carefully consider the output of LLMs to ensure they are not unfairly discriminating. In fact, until such a time as further research and investigation into their biases are performed, LLMs should not be used in sensitive areas where bias and discrimination are possible and likely to impact business decisions. On the other hand, LLMs can also be used to help actuaries identify and mitigate unfair discrimination, rather than to perpetuate it.
LLMs have a limited memory, meaning over time, they lose the context of the discussion or conversation and begin to hallucinate. This is more of an issue in assistance use cases where the user interacts conversationally with the LLM and less so with API-based interaction, where the context is normally restricted to one or two prompts. However, it is important to be aware of this limitation and to ensure that the LLM is not used in a way that requires it to remember information over a long period of time.
LLMs can also produce completely incorrect and fake information without a long conversation history. For example, the user can ask for assistance in a coding problem, and the LLM will generate a solution for using programming libraries that simply do not exist. Further, commercial models such as those provided by OpenAI are not pure implementations of the underlying LLM. Additional safety measures are included to make the results safer and more controlled. In fact, these measures have led the performance of ChatGPT to vary considerably over time (Chen et al., Reference Chen, Zaharia and Zou2023).
The use of LLMs in actuarial work may raise concerns about data security and privacy, especially when dealing with sensitive personal or financial information. Actuaries will need to ensure that they are using LLMs in a way that complies with data protection laws and regulations. As LLMs become more integrated into actuarial work, there may be a need for greater transparency and explainability in how these models work and how they arrive at their conclusions. This is particularly important when LLMs are used to make decisions that have significant financial or social implications.
Thus, it is imperative that where LLMs are used, the actuary carefully reviews the LLM itself, the provider, and their alterations and policies governing the LLM, and most importantly, to carefully review and monitor the results over time to appropriately manage the risks LLMs present. The use of LLMs in actuarial work will require ongoing monitoring and validation to ensure that the models are performing as expected and that the results are accurate and reliable. This may include periodic reviews of the models, as well as the development of validation frameworks and methodologies.
6.3. Professionalism
As with all work performed by actuaries, they must adhere to strict professionalism requirements. This includes the requirement to act in the best interests of the insurer and its policyholders and to ensure that their work is accurate and reliable. This is particularly important when using LLMs, as discussed in the prior section.
However, the treatment of risks and professionalism within this paper may not be sufficient for actuaries to fully understand the risks and professionalism requirements associated with LLMs. Rather, the purpose of this section is to simply highlight the discussion and bring forth actuaries’ responsibilities.
Several questions arise relating to the use of LLMs within actuarial work. For instance, how can actuaries ensure that the use of LLMs complies with professional standards and ethical guidelines? How can the risks associated with LLMs be effectively managed? How can actuaries ensure that the use of LLMs does not result in unfair discrimination or other adverse outcomes? These are complex questions that require careful consideration and further research. It is hoped that this paper will stimulate discussion and exploration of these important issues.
Finally, a grey area often emerges as to who is responsible for errors resulting from using AI. Viewing LLMs as a tool to assist the actuary, it is believed that there is an onus on the actuary to ensure that the results are accurate and reliable, just as one would when using a tool such as spreadsheet software or a word processor. Appropriate controls and risk mitigation actions should be put in place.
7. Conclusion
This paper has explored the potential applications of LLMs to actuarial work and insurance functions. Through a detailed consideration of the claims process and high-level consideration of other insurance functions, it has been demonstrated how LLMs can be used to improve efficiency and reduce human error. A framework was presented to identify whether an LLM is suitable for a given problem, and the use of LLMs was demonstrated in a number of case studies. Risks associated with LLMs and the professionalism requirements of actuaries when using LLMs were also discussed briefly.
The findings suggest that LLMs can significantly enhance the efficiency and accuracy of actuarial and insurance work.
By leveraging LLMs to improve efficiency and quality of work, while reducing human error, actuaries can focus on tasks that require their unique expertise and judgement. However, it is also recognised that the use of LLMs in actuarial work is not without challenges, which include the risk of errors and biases in the models, the need for careful review and interpretation of the output, and the ethical and professional considerations associated with the use of AI in actuarial and insurance work.
Looking ahead, it is believed that LLMs have the potential to greatly improve the abilities of actuaries and insurance functions both directly and indirectly. It is hoped that this paper will stimulate further research and discussion on this important topic, and actuaries are encouraged to explore the use of LLMs in their own work and to share their applications and experiences with the actuarial community.
In conclusion, while LLMs will not fundamentally change the way actuaries work, they can enhance the work of actuaries and the insurance industry as a whole, so long as professionalism requirements are adhered to closely and actions are taken in the best interests of the insurer and its policyholders.
Acknowledgements
Great appreciation is extended to Ronald Richman for valuable discussion, review, mentorship, and support for this paper and in general. Thanks are also extended to Anton Gerber for his detailed and careful review of the first draft of this paper and for his valuable feedback and comments which were incorporated to improve the overall quality of the paper.
Acknowledgement is given to the assistance of ChatGPT in the writing process of this paper. ChatGPT provided valuable assistance in drafting, editing, and reviewing the manuscript.
Appendix A. Accompanying Code Repository
Accompanying this paper is a code repository containing the code used in the case studies presented in this paper. The repository can be accessed at https://github.com/cbalona/actuarygpt-code.
Appendix B. Definitions
Appendix C. Excerpts of Assistant Conversations
This appendix contains excerpts of conversations between an actuary and ChatGPT. The 24 May 2023 version of the ChatGPT website Footnote 15 was accessed using the GPT-4 model.
C.1. Coding Assistant
The actuary is seeking assistance with their IBNR reserving work and requires assistance with the code and thinking.
Figure 18. Coding assistant conversation with ChatGPT part 1.
Figure 19. Coding assistant conversation with ChatGPT part 2.
Figure 20. Coding assistant conversation with ChatGPT part 3.
C.2. Model Understanding and Development
The actuary queries the LLM on an unfamiliar model and how to optimise it.
Figure 21. Model Understanding Prompt.
C.3. Problem-Solving
The actuary has a unique problem, and requires their solution to be reviewed.
Figure 22. Problem-solving conversation with ChatGPT.
C.4. Few-Shot Learning Demonstration
This section demonstrates few-shot learning to prime the LLM to produce an answer in line with the specified requirements. First, a conversation is presented with few-shot learning, where three examples of the expected output are provided. Following this, a zero-shot example is shown for comparison.
C.4.1. Few-shot learning example
Figure 23. Few-shot learning example with ChatGPT part 1.
Figure 24. Few-shot learning example with ChatGPT part 2.
Figure 25. Few-shot learning example with ChatGPT part 3.
C.4.2. Zero-shot learning example
Figure 26. Zero-shot learning example with ChatGPT part 1.
Figure 27. Zero-shot learning example with ChatGPT part 1.
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