AI in claims: tools of the trade

20th October 2026

Articles in the series

Explore the committee

NexGen Claims Leadership Group

Following earlier articles exploring the fundamentals and effective prompting and risks of AI, this article turns to the tools available to claims professionals throughout the claims handling process.

Artificial intelligence is moving rapidly from concept to practical application across insurance claims. We spoke to three major providers of claims management systems – DOCOsoft, Guidewire and Charles Taylor – to hear their thoughts on how AI is shaping the claims profession and where value can be delivered.

While technology is often discussed in terms of efficiency gains, the conversations with providers suggested the impact could be far broader. From automating administrative tasks and improving claims triage to unlocking portfolio-level intelligence, AI is increasingly being viewed as a strategic enabler rather than simply another technology tool. At the same time, all three providers stressed that human expertise, judgement and accountability remain fundamental to effective claims handling.

How AI is reshaping the claims profession

The strongest consensus among the three providers is that AI will fundamentally change how claims professionals spend their time.

Routine and repetitive activities such as document review, workflow management and triage are expected to become increasingly automated. This shift will allow handlers to focus more of their time on customer engagement, negotiation, decision-making and complex case management. Charles Taylor has described AI as becoming “a core component of the claims operating model rather than a standalone technology”, supporting claims teams by taking on repetitive administrative work and improving overall customer experience.

DOCOsoft believes the increased support from AI for routine activities will be particularly valuable given the talent challenges facing the London Market, as identified in the recent LMA Claims Talent Survey. By reducing the burden of repetitive administrative work, AI can help organisations make better use of scarce expertise while creating more engaging and rewarding roles for claims professionals.

Guidewire anticipates a significant shift in the London Market and specialty sector, where large amounts of time are currently spent reviewing bordereaux, policy schedules, endorsements, broker correspondence, and expert reports. The company expects AI to assume much of this “reading and reconciling” work, enabling specialists to concentrate on strategy, negotiation, and client advocacy. Looking further ahead, Guidewire sees agentic AI taking on multi-step activities such as requesting missing documentation, validating third-party data and drafting communications.

Beyond operational efficiency, there is a broader vision emerging. DOCOsoft feels that AI’s biggest long-term impact may come from unlocking information that has traditionally been held within unstructured documents such as emails and reports, allowing claims professionals to identify claims patterns and insights, transforming claims data into strategic intelligence that can inform underwriting, pricing, reserving and risk management decisions across the business.

Where AI delivers value today

AI can have application across the entire claims lifecycle, however, DOCOsoft highlights “the key is to apply the technology to the right problems, with clear objectives and measurable outcomes so that organisations can define what success looks like and assess whether real value is being delivered”.

The most immediate benefits are often seen during claims intake and triage. AI can process incoming emails, reports, claim notifications and supporting documents, extracting key information and routing claims appropriately. All providers highlighted the ability of AI to remove routine and repetitive tasks, and classify, prioritise, and triage work based on risk, urgency and complexity to help organisations deploy resources more effectively.

Claims understanding and summarisation were also identified as major opportunities. Claims handlers frequently handle files containing extensive correspondence, reports and historical activity. Guidewire stated that AI can deliver value by consolidating this information into concise, role-specific summaries, reducing the time needed to understand a claim’s status, history and outstanding actions.

The growing importance of portfolio-level analysis was also highlighted by all providers. DOCOsoft sees significant value in using AI to analyse claims data at scale, identifying trends, correlations and claims leakage that would be difficult for humans to detect manually, arguing that even small improvements in claims outcomes can generate substantial financial benefits when applied across large books of business.

Guidewire similarly emphasised AI’s role in reserving, settlement strategy, and leakage control. By applying predictive analytics across large datasets, insurers can improve reserve accuracy, identify potential fraud or subrogation opportunities, and gain early visibility of emerging risks. They also believe AI will strengthen claims oversight by giving managers near real-time insight into portfolio performance, complaints trends and litigation risks.

Charles Taylor suggested that AI could remove routine and repetitive tasks, allowing the claims handlers to spend more time on customer engagement, but also echoed the management advantages of AI-driven insights, highlighting that AI can help improve visibility into claims trends, operational bottlenecks, leakage and performance metrics, supporting more informed business decision-making

Available AI capabilities

While the industry’s ambitions for AI are significant and plans continue to evolve, many valuable capabilities are already available.

Guidewire

Guidewire has focused on embedding AI directly into claims workflows. Its capabilities include predictive analytics through Claims Intel models, which generate risk and severity scores to support claims prioritisation and more accurate reserving decisions. The company also offers agentic claims summarisation, allowing claims handlers to familiarise themselves quickly with complex files. Claim handlers can also benefit from an embedded knowledge assistant to query organisational guidance and procedures using natural language.

DOCOsoft

DOCOsoft’s AI capabilities focus on applying technology to real-world practical challenges faced by claims handlers. These have been developed over many years, with close ongoing partnership with leading carriers. Their current offering includes the ability to extract key information from documents and emails, categorise incoming material and flag issues requiring attention from the claims handler, claim prioritisation and triage based on factors such as urgency, complexity and risk, claims summarisation, support for faster, better-informed human decision-making, and analysis of claims at scale to identify patterns, emerging trends and potential leakage.

Charles Taylor

Charles Taylor has recently undertaken workshops with more than 60 users to understand the market demand. Claims summarisation, knowledge transfer, intelligent triage and prioritisation, analytics, portfolio oversight and the enrichment of structured and unstructured data emerged as the key priorities. Working with Microsoft as a trusted partner to bring these ideas to life, development is underway and The company indicated that further announcements were expected later in 2026 and in early 2027.

Across all three organisations, a common theme is that the objective is not simply to introduce new technology, but to improve the way claims professionals work within their existing environments.

Why human expertise remains essential

Despite the advances in AI, the three providers are united in their belief that some aspects of claims handling should remain firmly under human control.

Coverage decisions, settlement strategies, reserving judgements and disputed claims all involve significant legal, financial and reputational consequences. While AI can help gather information, identify relevant evidence and highlight possible outcomes, the providers agreed that accountability for these decisions should remain with human claims professionals.

Charles Taylor identified customer support, complex judgement, settlement negotiations and regulatory oversight as areas where human expertise remains indispensable, advising they see the role of AI as “augmenting, rather than replacing claims professionals”.

DOCOsoft stressed the importance of explainability, traceability and effective human oversight. The company argues that claims professionals must be able to understand and challenge AI outputs before relying on them in decision-making, while also sharing Charles Taylor’s thoughts on customer support, highlighting the importance of empathy, communication and relationship management, particularly in complex or sensitive claims situations. Guidewire points specifically to the complexities of London Market business, where bespoke wordings, multi-carrier placements and long-standing broker relationships require nuanced judgement. Although AI can provide support, they believe that at least in the intermediate term, coverage and wording decisions, major loss strategy and negotiations, client care during difficult claims, and decisions involving sanctions, contentious declinatures or ESG considerations will remain firmly human-led

Conclusion

A clear picture of the future is emerging. AI is likely to take on more of the processing, organisation and analysis of claims information, helping insurers improve efficiency while generating deeper operational and portfolio-level insights. Claims professionals need not see these developments as a threat: judgement, negotiation, empathy and accountability will remain central to successful outcomes.

Rather than replacing claims professionals, AI has the potential to make them more effective. To realise these benefits, insurers will need to combine technology with human expertise, using AI to reduce administrative burdens while enabling claims teams to focus on work that creates the greatest value for customers, brokers and the business

Acknowledgements and provider contacts

The LMA thanks Charles Taylor, DOCOsoft and Guidewire for contributing their perspectives to this article.

Up next: “Trust, but Verify” explores the key areas in which claims professionals should exercise care when using AI.

Articles in the series

Explore the committee

NexGen Claims Leadership Group

AI and the claims profession: asking the right questions

15th October 2026

Articles in the series

Explore the committee

NexGen Claims Leadership Group

In the first article in this series, AI fundamentals: history, terminology and limitations, we explored the origins of AI and the terminology surrounding it. This article considers how claims professionals can improve the accuracy, relevance and usability of AI-generated output by providing clear, structured and purposeful instructions, known as prompts.

Poorly structured prompts can produce incomplete, misleading or overly generic responses, potentially introducing risk rather than reducing it. Effective prompts establish the task, context and standard of output required. As AI models evolve, prompting approaches may also need to change.

Why effective prompting matters

A well-constructed prompt helps an AI tool respond in a way that aligns with your objectives. Consider whether a colleague with no prior context would understand what you needed from the same instructions. If not, the prompt may need more detail. Clear guidance can help the model return more focused, concise and actionable information.

For example, “summarise this claim” may produce a high-level response that omits important details. A more useful instruction would specify the audience (such as the Head of Claims), the level of detail (such as a concise bullet-point summary) and the issues to address (such as liability and quantum).

The rules of prompting

Different AI models may respond differently to prompt length, structure and examples. Check the guidance for the particular tool and version approved by your organisation, and test whether the output meets your needs.

Tailoring prompts to the tool

Claude: Give clear context and instructions. Ordered steps and examples may help shape the format and tone of the response.

GPT models: Focus on the desired outcome and success criteria. Start with a concise, clearly structured request, adding detail when needed.

Microsoft Copilot: Specify the goal, context, expected format and, where possible, the relevant documents or data sources.

Five elements of a useful prompt

1. Goal: Define the end objective, such as producing an executive claims summary.

2. Context: Give relevant background, including the audience and purpose.

3. Success criteria: Explain what a good response should achieve, such as being concise and clearly structured.

4. Constraints: Set boundaries, including length, tone, output format and exclusions.

5. Source: Identify the documents or data the AI should use to reduce unsupported claims.

AI in the claims environment

“Using the attached letter, re-write it in plain English for the insured. This is a claims update, so ensure the tone is clear, supportive, and easy to understand. Summarise our current position, key facts, and any required next steps. Retain all policy conditions displayed in italics exactly as written and present the letter in a Word document format.”

Why it works:

Carefully balances clarity, tone, and compliance requirements. The instruction to preserve policy wording verbatim ensures legal or contractual language is not altered. Simplifying the surrounding text improves customer understanding, producing a compliant and customer-friendly document.

Across these examples, effective prompts define the objective, provide relevant context and set suitable constraints. This helps make the resulting output more relevant and easier to review and use.

Summary

By using structured prompts, adapting their approach to different AI tools and maintaining appropriate oversight, claims professionals can use AI more effectively in day-to-day activities.

AI can improve efficiency and support quality, but it is not a substitute for professional judgement. All outputs should be reviewed and validated before use. Claims professionals must also follow their organisation’s policies on AI use, confidentiality and information security, including using only approved tools for sensitive material.

Editorial note: AI was used in preparing the original article to define what a good prompt looks like and how the prompt examples could be improved.

Up next: “When the Algorithm Gets It Wrong” exploring how hallucinations can affect the use of AI in claims.

Articles in the series

Related content

NexGen Claims Leadership Group

AI fundamentals: history, terminology and limitations

Articles in the series

Explore the committee

NexGen Claims Leadership Group

Across the London market, claims handlers are using AI tools in different ways. Some use them to summarise lengthy documents and draft correspondence, while others are beginning to incorporate them into coverage analysis, triage and reserving.

In many cases, adoption is moving faster than understanding. This gap is what this article series aims to address. The first article explores how these tools developed, explains key terminology and introduces some of the risks associated with their use

Where did it come from?

AI has been used in insurance for many years. Actuarial, pricing and claims teams have long used machine learning to identify patterns in historical data, flag anomalies and assess risk. Claims fraud detection, for example, has relied on systems trained to recognise patterns associated with potentially fraudulent claims. Many traditional systems were designed primarily for specific tasks using structured data, such as spreadsheets.

In 2017, researchers at Google published a paper introducing the Transformer architecture. Its attention-based approach made it possible for models to process relationships between words across a passage more effectively. This development helped lay the foundations for language models capable of working with unstructured text, including loss adjusters’ reports, claims submissions and manuscript wording clauses.

The next major milestone was wider public access. In November 2022, OpenAI launched ChatGPT, making conversational generative AI accessible through a straightforward interface. Its rapid adoption brought the potential applications and implications of AI into much wider discussion.

The language of AI: what you need to know

The adoption of AI has introduced a considerable amount of jargon. The explanations below cover terms you are likely to encounter in presentations, team discussions and boardrooms.

The foundations

Model: The underlying system trained to perform a particular task. When your firm licences an AI-powered product, deploys a claims tool or uses ChatGPT, it is interacting with one or more AI models. Different products may use models developed internally or supplied by third parties.

Large language model (LLM): A type of AI model trained on large amounts of data to understand and generate language. Many newer models can also work with images, audio and structured data. An LLM generates text by predicting the next token, which may be a word or part of a word, based on the context it has received. Models vary in their capabilities, design and limitations. Training and operating advanced AI models can also require substantial computing infrastructure, including specialist chips, electricity and cooling resources, creating risks and insurance considerations of their own.

How it learns

Training: The process through which a model develops its capabilities by learning patterns from data. As an analogy, imagine a junior claims handler studying a vast collection of claims files, policy wordings, coverage opinions, legal textbooks and loss adjusters’ reports to understand how insurance concepts and language relate to one another. Training is not the same as storing a searchable copy of every document, although models can sometimes reproduce information encountered during training. The model learns statistical patterns that help it generate responses to new instructions.

How you interact with it

Prompt: The instruction or question you give an AI tool. If the model were a claims colleague, the prompt would be your brief: what you need, why you need it, the format required and any constraints. Clear and specific prompts generally make it easier to obtain useful outputs.

Generative AI (GenAI): AI capable of creating content, including draft letters, summaries, code and analytical narratives. This differs from many traditional machine-learning applications, which were primarily designed to classify, predict or detect patterns rather than generate content.

Agentic AI: AI systems designed to plan and carry out a sequence of actions, potentially using other software and tools. In a claims setting, a system could be configured to open a new claim, review attachments, update a claims platform and prepare correspondence for review. Depending on its design, it may perform several steps without human approval at each stage. This raises important questions about governance, permissions and oversight, which the series will explore further.

The risks

Hallucination: An AI-generated statement that is false or unsupported but presented as though it were accurate. Examples include an invented legal precedent, an incorrect policy clause or a fabricated fact. These errors can have serious professional consequences, so AI-generated information must be checked against reliable sources, particularly where it informs claims decisions or external communications.

Context window: The amount of information a model can consider within a particular interaction. When documents or conversations exceed that limit, a tool may truncate, exclude or otherwise fail to use some material. This can result in incomplete analysis that is not immediately obvious. Context windows are expanding, but the issue remains important when handling lengthy claims files. Some systems divide documents into smaller sections, or chunks, although this can introduce challenges in maintaining context across sections.

Retrieval-augmented generation (RAG): An approach that allows an AI system to retrieve information from specific sources, such as an organisation’s claims files, policy wordings or handling guidelines, and use it when generating a response. Well-designed retrieval can make outputs more relevant and easier to verify, particularly where the system provides references to source documents. It can reduce some errors, but it does not eliminate the need for human review.

Key takeaways

AI is not a single technology. Copilot, the AI component of a claims system and a public chatbot may use different models, data sources and controls. Their capabilities and limitations should be assessed separately.
AI-generated answers can sound convincing even when they are wrong. Material facts, policy interpretations and other consequential outputs require appropriate verification and professional oversight.
Understanding how to prompt effectively, manage source material and recognise limitations such as context windows will become increasingly important as agentic AI develops.

Up next: Artificial intelligence and the claims profession: asking the right questions, explores effective prompting in a claims context and why the quality of an instruction shapes the usefulness of the output.

Related content

Explore the committee

NexGen Claims Leadership Group

LMA Capital Provider Data Requirements

6th October 2026

Download the materials

The LMA Capital Provider Data Requirements establish a standardised set of information that can be provided to members’ agents and third-party capital providers. They have been designed to address the loss of consistency and comparability that followed the replacement of elements of the QMA process with tagged financial statements.

These requirements are only relevant for managing agents that manage syndicates supported by third-party capital. They are not intended to introduce new reporting obligations and are based on data that is already generated for Lloyd’s reporting, regulatory purposes or internal management. To add further flexibility, transitional options have been incorporated to accommodate differing reporting capabilities and processes across managing agents.

Benefits for managing agents

For managing agents, the framework provides a clear market standard that removes the need to respond to multiple bespoke information requests from different members’ agents. It enables firms to support third-party capital providers without having to continue producing the previous QMA pack and enables firms to complete process redesign to realise the full benefits of the tagged financial statement production.

Benefits for members’ agents

For members’ agents and capital providers, the framework restores access to key information that has become less consistent since the reporting changes. It also provides data specifically designed around investor requirements, reducing reliance on proxy measures or differing interpretations of available financial information.

Adoption

Adoption is expected to commence for Q4 2026 or no later than Q1 2027. Although no formal timetable is being stipulated, managing agents should liaise with members’ agents to explain how and when the information will be supplied and the transitional options that will be used. The duration of the transition period has not been prescribed and will be kept under review alongside future developments in Lloyd’s reporting requirements.

The LMA will monitor adoption and effectiveness through ongoing engagement with both managing agents and members’ agents. Feedback on the information being requested and received will be used to refine the requirements over time and to ensure that they remain aligned with the evolving needs of the Lloyd’s market and its capital providers.

Capital Provider Data Requirements

Guidance on the framework, scope and reporting requirements.

For more information about the Capital Provider Data Requirements, please contact Paul Davenport, Finance and Risk Director at the LMA.

Download the materials

Related content

Third Party Capital Committee and Members’ Agents Committee

Claims Capability Framework

29th July 2026

Related content

The Claims Capability Framework for new entrants sets out the skills and experiences a claims adjuster is expected to develop within their first two years in the London market. By creating this framework, the market aims to demonstrate its commitment to delivering high-quality foundational learning for the claims profession.

What is the framework?

The framework provides a clear pathway for claims capability and aims to raise the bar by delivering consistent foundational learning for the claims profession. It consists of:

Claims capabilities – the baseline level of technical knowledge and skill development for new claims entrants across the London market, to be achieved within two years.
Learning resources – examples of how organisations might support the development of the claims capabilities. These are suggestions, not requirements.

How was it developed?

This framework has been co-created by market participants, ensuring it reflects both current practice and emerging skill requirements. Thirty-seven individuals from 25 market firms contributed to its development – with representation from Heads of Claims, Claims Managers, emerging claims professionals and HR/L&D specialists across LMA and IUA membership and the broking community. Rosie Chopra, Course Director of MSc Insurance & Risk Management at Bayes Business School, also supported the process through facilitation and bringing an academic perspective.

Market adoption

By continuing this collaborative approach, we hope the framework becomes a pathway the market can confidently endorse and work towards when developing new claims professionals. Our goal is to ensure these claims capabilities are applied consistently across the market. Many firms will already cover several of these areas within their existing frameworks. How you choose to deliver and embed the capabilities will be unique to your organisations.

Support and guidance

We have created a progress tracking document designed to help individuals track their own progress and capture reflections.

Each tab represents a section of the framework and breaks down each of the claims capability statements. Individuals can use the document to self assess progress, capture evidence points and supervisor comments, to the extent that is useful.

We have also produced a Frequently Asked Questions document.

Download the Claims Capability Framework

Download

Claims Talent Survey Results

16th July 2026

Foreword

In September 2025, the LMA claims team issued a follow-up to the 2023 Heads of Claims Talent Survey. When the original survey was conducted, talent had been recognised as a concern across the claims community, but the scale and nature of the challenge were not fully understood. The 2023 results provided critical insight, shaping the work of the NexGen Claims Leadership Group and informing a more coordinated, market-wide response.

That response has included initiatives such as Forage, the development of the first stage of the Claims Capability Framework and the promotion of existing market programmes including London Insurance Life and the LMG Futures Academy. Throughout, our approach has focused on supporting and amplifying existing efforts rather than duplicating them. Collectively, this work has raised awareness of the risks associated with claims talent and helped establish investment in talent as a strategic priority across the market.

Two years on, it was timely to revisit the survey to assess progress, identify shifts in the landscape and explore emerging themes. We received responses from over 50% of Lloyd’s managing agents. While this provides a strong and representative picture of the market, it is worth noting that the findings do not reflect the complete data set, and some perspectives will inevitably remain outside the sample.

The results are clear: talent remains a priority for claims leaders. While many of the challenges identified in 2023 persist, there is now a more nuanced understanding of their underlying drivers, with themes relating to the impact of AI on skills and workforce planning; the potential loss of deep expertise as experienced professionals retire; and continued pressure on the mid-career talent segment.

As always, we welcome your feedback. If these findings prompt questions or insights, please do get in touch.

Janine Powell
Claims Director
LMA

Key findings

The findings point to encouraging signs at the pipeline’s entry level, reflecting the market’s increased focus on new entrants. However, persistent recruitment challenges and growing concern about the loss of experienced professionals, many of whom are approaching retirement age, are placing increasing pressure on the mid-career segment, and it is here that the most urgent work remains.

Data indicates an increase in new entrants (individuals with under three years’ experience) alongside a reduction in highly experienced professionals (those with over 15 years’ experience).

There has been increased recruitment of school leavers, graduates and individuals from outside the insurance industry, alongside more internal promotion into senior roles. Notably, “outside of industry” hiring strategies have broadened – from a previous focus on solicitors to a wider emphasis on transferable skills such as data, risk management, digital capability and communication.

Awareness and use of market initiatives – particularly London Insurance Life and Forage – have increased significantly as part of recruitment strategies.

Recruitment challenges continue, including a limited talent pool across classes, rising salary expectations, and ongoing competition for talent. Mid-career professionals appear particularly constrained. Flexible working arrangements (office vs home) have also emerged as a new factor.

The key risks and opportunities over the next 3–5 years remain broadly consistent with 2023. However, there is now greater specificity in how these are articulated, particularly regarding:
- the impact of AI on skills and workforce planning
- the potential loss of deep expertise due to retirement
- continued pressure on the mid-career talent segment.

1. Claims population

Heads of claims were asked to provide details of the seniority, roles and experience levels within their claims teams.

Initial signs suggest a shift in the demographic profile of the claims community; however, population data should be interpreted with caution, as the dataset does not include all managing agents.

The data indicates a notable increase in new entrants, coupled with a downward shift in the proportion of individuals with over seven and fifteen years’ experience, reinforcing anecdotal concerns among heads of claims regarding the loss of experience.

Notes

Population data should be interpreted with caution, as the dataset does not include all managing agents.

All respondents had London-based claims teams. 19% of respondents had claims employees in UK regional offices. 33% had claims employees in international offices (predominately US and APAC).

Some roles have not been accounted for by heads of claims in the data, including: assistant roles, counsel, quality assurance roles and strategic teams.

2. Attracting talent

Recruitment conditions across the market have changed little since 2023. There has been limited movement across classes in the overall recruitment experience, and the challenges identified two years ago remain largely intact. This raises a question the market may need to confront directly: are current recruitment models fit for purpose?

Heads of claims are saying:

Salary expectations continue to outpace experience levels with compensation not considered commensurate with the experience candidates bring.

The 5–7 year and 15+ year experience brackets remain the hardest to recruit into, with a continued trend of candidates seeking senior roles without the requisite background.

The available talent pool remains limited across all lines, with niche markets feeling this most acutely.

There remains concerns about “poaching”, with investment in training and development undermined by the difficulty of retaining those who have benefited from it.

A reluctance among candidates to commit to four days a week in the office has emerged as a new and meaningful constraint on recruitment since 2023.

Survey respondents said:

Survey respondents were asked to give details of how they attract and retain talent in claims.

69% of recruitment accesses talent from within the existing market talent pool (down from 74% in 2023).
University graduates, school leavers and outside of industry now accounts for 52% of junior roles (an increase from 37% in 2023).
Compared to 2023, the biggest drop in recruitment was of experienced adjusters from London market moving from 22% to 17%.
“Outside of industry” hiring strategies have broadened with a wider emphasis on transferable skills such as data, risk management, digital capability and communication.

Traditional sourcing methods decreased compared to 2023: the use of recruitment agencies has seen the largest decrease, of 16%, while internal recruitment teams also saw a decrease of 6%.
In addition to an 11% increase in usage of LinkedIn, 33% of respondents have used Forage, London Insurance Life and/or other social media platforms to source talent.
The results indicate increased awareness and use, across the claims community, of the LMG’s London Insurance Life to advertise early careers roles (at no cost to the market).

3. Looking ahead

The risks and opportunities identified by heads of claims over the next 3–5 years remain broadly consistent with those of 2023, but with a sharper and more specific articulation of where the pressure is greatest. Three themes have come into clearer focus: the impact of AI on skills and workforce planning; the potential loss of deep expertise as experienced professionals retire; and continued pressure on the mid-career talent segment.

When asked whether their view of future risks and opportunities had changed, heads of claims pointed to two overriding factors: the pace of technological change and increased automation, and a perceived reduction in knowledge and expertise within the market. Together, these are reshaping expectations of what good looks like in a claims professional.

The skills most frequently cited as critical for the future reflect this shift:

data analytics
technical expertise
customer communication
relationship management
portfolio management
adaptability and strategic thinking.

The presence of both technical and relational skills on this list is notable, suggesting that while technology will change how claims work is done, the human dimensions of the role remain highly valued. The challenge for the market is developing professionals who can credibly deliver both.

4. Risks and opportunities

The risks and opportunities identified by heads of claims have sharpened since 2023, with AI, building new skills and capability and the mid-career gap emerging as the defining features of the next 3–5 years.

Risks identified	Opportunities identified
The advancing retirement of experienced claims professionals presents a significant risk to deep market expertise, with limited succession pipelines to absorb the loss of institutional knowledge.	AI and automation, if embraced effectively, free claims professionals from more routine tasks – creating space for higher-value problem solving, judgment and expertise.
The mid-career gap remains a structural concern, with the number and types of roles increasingly misaligned with the available talent pool.	The digitalisation of claims creates genuine demand for new technical profiles (data analysts, technology specialists and digitally fluent adjusters) opening the door to talent pipelines that did not previously exist.
AI and digitalisation risk makes elements of claims adjusting less stimulating, potentially deterring talent at the point of entry or prompting early exits.	New and emerging insurable risks offer the opportunity to diversify into new areas, attracting candidates motivated by complexity and innovation.
The shift in required skillsets towards data, analytics and technology means existing talent may find their capabilities less relevant without sustained investment in development.	Claims has a growing story to tell as a career destination; one that is central to protecting customers and delivering on the promise of insurance, with increasing commercial visibility in winning and retaining business.
Underinvestment in training, compounded by competitive recruitment environment, creates a disincentive to develop talent and risks a race to the bottom across the market.	A sharper market focus on data creates opportunity to build new capability and attract professionals from adjacent fields.

Claims initiatives

Market initiatives

Understanding International Bodily Injury Award Trends

22nd April 2026

Related content

Explore the committee

International Liability Business Panel

The LMA International Bodily Injury Index (BII) provides a comprehensive view of bodily injury compensation outcomes across a broad international sample. By applying a consistent set of injury scenarios across multiple jurisdictions, it enables like-for-like comparison of indicative award values and helps track how those values change over time.

The Index is designed to be used by underwriters, claims professionals and risk specialists in managing agents to help in understanding bodily injury exposure across jurisdictions. It does not predict individual claim outcomes; actual claims depend on the specific facts of the case, liability, evidence, policy terms and local procedural requirements.

This overview focuses on interpretation and context. Detailed figures, jurisdiction-level data and scenario-specific analysis are available to subscribing managing agents.

The value of the International Bodily Injury Index lies in its ability to cut through anecdote and provide a consistent, evidence-based reference point across jurisdictions.
– David Fitzpatrick, Chair of the International Liability Business Panel

Purpose and value of the index

The International Bodily Injury Index provides a structured, transparent reference point for understanding how bodily injury awards vary internationally and how the sums awarded evolve over time. By applying consistent scenarios across a wide jurisdictional sample, the Index supports more informed discussion of claims cost trends and their drivers.

The 2024 to 2025 comparison establishes a baseline against which future movements can be assessed, allowing meaningful differentiation between gradual change and genuine structural shifts as the dataset develops over time.

Scope of the index

The BII covers:

96 jurisdictions, excluding the US, covering a broad international sample across multiple regions.
56 local currencies.
Eight standardised bodily injury scenarios – from minor injuries to catastrophic injuries and fatal outcomes.

The same claimant profiles and injury definitions are used in every jurisdiction. This standardisation allows differences in indicative award values to be compared across jurisdictions and over time, where data is available.

All figures are shown in local currency to reflect how awards are assessed and settled in practice within each jurisdiction.

Figure 1. International coverage of jurisdictions included in the LMA International Bodily Injury Index where year-on-year data is available (i.e. jurisdictions where values in both 2024 and 2025 are available).

Figure 1 note: Jurisdictions not shown may still be included in the Index but do not have complete data for both years yet.

Data availability and interpretation

The Index prioritises transparency and comparability. As a result, not every jurisdiction has complete data across every scenario and year.

Year-on-year analysis is based only on like-for-like comparisons, meaning jurisdictions where the same scenario has values in both 2024 and 2025.

What the data shows from 2024 to 2025

Data note

This analysis reflects the dataset as at 15 January 2026 and is based on jurisdictions with values available in both 2024 and 2025 at that time. The Index is updated periodically as additional jurisdictional data becomes available. Subsequent updates may expand the like-for-like sample and refine the distribution of movements.

Coverage for year-on-year analysis

Across the eight scenarios, between 59 to 60 jurisdictions currently have values available in both 2024 and 2025, depending on data availability for the specific scenario. These jurisdictions form the basis for year-on-year movement analysis.

Typical year-on-year movement

Across jurisdictions where values are available in both 2024 and 2025 (the like-for-like set used for year-on-year comparison), the median year-on-year change is modest:

Minor or superficial injuries: increase of 1%.
Broken limb injuries: increase of 1%.
Serious and catastrophic injuries (including amputation, loss of sight, psychiatric injury, brain injury and paralysis): increases typically between 2% and 3%.
Fatal outcome scenario (full awareness of death): increase of 3%.

The distribution shows that most jurisdictions experienced incremental, rather than dramatic movement, between 2024 and 2025. However, a small number of jurisdictions show more pronounced year-on-year movements. Ten to fifteen jurisdictions fall into the “large increase” band (more than 10% year-on-year), depending on the scenario.

For example, in the minor or superficial injuries scenario, jurisdictions such as Uzbekistan and Japan show large year-on-year increases, while in the paralysis scenario, large movements are seen in jurisdictions such as the UAE and Jamaica. These larger shifts are typically linked to discreet local developments, such as legislative change, court practice shifts or updated valuation reference points.

Distribution and variability

Although median movements are low, the distribution of outcomes is wider for some scenarios. Interquartile ranges vary materially, particularly for minor injuries and paralysis. This reflects differing local dynamics rather than a uniform global trend.

Across scenarios, a significant proportion of jurisdictions show no year-on-year change at all. Depending on the scenario, between 15 and 25 jurisdictions remain unchanged within the like-for-like sample.

Direction of change

Increases are more common than decreases, but decreases do occur:

In minor injury scenarios, 53% of jurisdictions showed increases, 5% decreased and 42% were unchanged.
In fatal outcomes scenarios, 64% increased, 5% decreased and 31% were unchanged.

This demonstrates that bodily injury awards are not moving uniformly in a single direction across all jurisdictions.

The charts below are derived from the International Bodily Injury Index. They illustrate high-level patterns and do not display detailed figures or jurisdiction-level values. The two charts present complementary views of year-on-year movement. Figure 1 shows the scale of change by grouping jurisdictions into movement bands, while Figure 2 shows the overall direction of movement across jurisdictions.

Figure 2. Distribution of year-on-year movements by injury scenario
(like-for-like jurisdictions only)

Figure 2 note: For each scenario, jurisdictions are grouped into bands based on the year-on-year percentage change between 2024 and 2025 in the indexed award value, for like-for-like jurisdictions only. Decrease means values decreased year-on-year. Stable means 0% change. Moderate increase means an increase greater than 0% and up to 10%. Large increase means an increase greater than 10%.

Figure 3. Direction of year-on-year change by injury scenario
(like-for-like jurisdictions only)

Figure 3 note: For each scenario, jurisdictions are categorised based on the year-on-year movement between 2024 and 2025 in the indexed award value, for like-for-like jurisdictions only. Increase means values increased year-on-year. No change means values were unchanged. Decrease means values decreased year-on-year.

What the data shows is not a single international story, but a pattern of stability for many jurisdictions alongside targeted areas of change driven by local legal and economic factors.
– Chris Mather, Senior Executive, Technical Underwriting at the LMA and Secretary of the International Liability Business Panel

Step changes and outliers

As set out above while the median year-on-year changes are modest and most jurisdictions fall within the stable or moderate movement bands, a smaller group falls into the “large increase” band, defined as movements of more than 10% year-on-year.

Examples observed in the data include:

In a small number of jurisdictions, year-on-year movements fall into the “large increase” band (more than 10% year-on-year), representing a minority of the like-for-like set. These more pronounced shifts tend to be concentrated in higher-severity scenarios and are more consistent with discrete local developments than with gradual inflation.
Larger step changes are more commonly seen in higher-severity scenarios (for example, paralysis and amputation) where local valuation frameworks can shift materially.
Decreases are less common than increases and are limited to a handful of jurisdictions per scenario, with some of the more notable decreases appearing in minor or superficial injury scenarios.

These outliers can skew the overall average when year-on-year percentage changes are summarised across jurisdictions. The analysis compares two figures per jurisdiction (2024 and 2025) and calculates a year-on-year percentage change for each jurisdiction in the like-for-like set. These jurisdiction-level percentage changes are then summarised across that set. To avoid distortion from outliers, this overview presents the median and the distribution
of movements rather than relying on a single average figure. Larger shifts may also point to discrete local developments, such as legislative change, shifts in court practice, updated damages guidelines and valuation reference points, influencing outcomes in particular jurisdictions.

Why this is significant

Stability is itself a finding

The data shows that international bodily injury awards are not experiencing uniform escalation year-on-year. Across scenarios, between 15 and 25 jurisdictions show no year-on-year change within the like-for-like samples. For example, 42% of jurisdictions remain unchanged in the minor injury scenarios and 31% unchanged in the fatal outcome scenario. In an environment where social inflation is often discussed in broad terms, the Index demonstrates that the reality is more uneven and jurisdiction specific.

Inflationary pressure is local, not universal

Where increases occur, they are unevenly distributed. The concentration of changes within specific jurisdictions suggests that local legal frameworks, court practice, economic conditions and claims environments are the primary drivers of movement rather than a single international trend.

Step changes indicate structural shifts

Large year-on-year movements in a small number of jurisdictions are more consistent with structural changes than with gradual inflation. For example, in the paralysis scenario, a small number of jurisdictions show year-on-year increases of more than 100%, including the UAE, Uzbekistan and Jamaica. Isolated decreases also occur, particularly in the minor or superficial injury scenario, for example, China and Queensland, Australia.

Direction of travel matters

The presence of decreases and unchanged values alongside increases demonstrates that bodily injury awards are not a one-way ratchet. They can stabilise or fall as well as rise, depending on local conditions.

Limitations and context

The scale of the Index is a strength, but it requires careful interpretation:

Blank fields reflect unknown data, not a 0 value.
Figures are indicative benchmark estimates, not claim predictions, developed through specialist legal input and jurisdictional insight.
Local context remains essential and is captured through jurisdiction-specific notes where available.
The Index is not a pricing or reserving tool and should not be used as a substitute for claim-specific analysis.
The Index is not intended to set market benchmarks, tariffs or pricing levels, nor to replace jurisdiction-specific legal advice.

Key definitions

Bodily injury scenario

A standardised, injury outcome used consistently across all jurisdictions. Each scenario is based on the same claimant profile and injury description to support comparability.

Like-for-like comparison

A comparison between years that includes only jurisdictions where a value is available for the same scenario in both 2024 and 2025. Jurisdictions without data in one of the years are excluded from year-on-year analysis for that scenario.

Year-on-year (YoY) change

The percentage change between the 2024 and 2025 indicative values for the same scenario and jurisdiction. Year-on-year analysis is calculated only on a like-for-like basis.

Local currency

Indicative values are shown in the currency used within each jurisdiction. Figures are not converted into a single reference currency to avoid masking local economic and legal dynamics.

Blank data field

A blank field indicates that the relevant value is unknown or unavailable for that jurisdiction and year. It does not mean that the value is zero or that no award is payable.

Indicative value

A benchmark figure reflecting informed market understanding for a defined scenario. Indicative values are not claim outcomes and should not be interpreted as pricing, tariffs or guaranteed awards.

Jurisdiction

A country or legally distinct legal system included within the Index. In some cases, (for example, Australia and Canada, we have dealt with different states within the country as their rules and awards can differ materially.)

Methodology (how year-on-year movement is calculated)

Year-on-year movement is calculated using the LMA International Bodily Injury Index’s standardised scenarios, comparing 2024 and 2025 values on a like-like basis. For each scenario, jurisdictions are included only where values are available in both years.

The year-on-year percentage change is calculated as:

(2025 value – 2024 value) ÷ 2024 value x 100

Subscribe to access the full dataset

Subscribing managing agents receive full access to the Index, including:

Scenario-level data and jurisdiction-specific insights.
Like-for-like year-on-year comparisons and distribution metrics.
Tools to support deeper assessment of bodily injury exposure and emerging trends.

To request access, please contact Chris Mather, Senior Executive, Technical Underwriting at the Lloyd’s Market Association.

Related content

Explore the committee

International Liability Business Panel

Underwriting Talent Summit 2026

29th March 2026

In 2025, we rang the bell on a critical challenge facing the London market: the female underwriting leadership pipeline was going backwards. This year, we move the conversation forward.

The 2026 Underwriting Talent Summit will focus on progression. If we are building a pipeline of talented female underwriters, how do we ensure they reach the most senior leadership roles? What barriers remain? What do future female leaders need from their employers? And what can senior underwriting leaders do differently to create lasting change?

Bringing together CEOs, CUOs and senior underwriters from across the market, this summit will explore the practical actions, leadership behaviours and organisational cultures that enable talent to thrive and progress. Our panellists and speakers will reflect on the reality of building a career in the London market: the achievements, the challenges and how they created their own pathway to leadership.

Agenda: 12 November 2026

09.00	Welcome and registration
09.30	Opening remarks: reflections, progress and the path ahead Sheila Cameron, CEO, LMA
09.45	What matters most? Perspectives from underwriting leaders of the future Erin Bargate, Divisional Director of Casualty, Hiscox Catherine Foster, Head of London Market Upstream Energy, Sompo Georgina Rothman, Underwriting Portfolio Director, Asta Rachel Webber, Head of Reinsurance UK, Canopius Sydonie Williams, Head of International Cyber Risks, Beazley
10.30	Keynote: AI: the technology that might finally change who gets to the top Tracy-Lee Kus, Co-CEO EMEA, Aon Rachel Turk, Chief of Performance & Strategy, Lloyd’s
11.15	Networking and coffee breakout
12.00	How they really got there: busting the myths of senior leadership Adam Beatty, CEO, Nephila Duncan Dale, CEO, Dale Underwriting Partners Kate Markham, CEO, Hiscox London Market Heather McKinlay, Non-Executive Director Vinay Mistry, Chief Commercial Operations Officer, Apollo Nick Moore, CEO, Argenta
12.45	Closing remarks Sheila Cameron, CEO, LMA
13.00	Close

Speakers

Erin Bargate

Divisional Director of Casualty
Hiscox

Adam Beatty

CEO
Nephila

Sheila Cameron

CEO
LMA

Duncan Dale

CEO
Dale Underwriting Partners

Catherine Foster

Head of London Market Upstream Energy
Sompo

Tracy-Lee Kus

Co-CEO
Aon

Kate Markham

CEO
Hiscox London Market

Heather McKinlay

Non-Executive Director

Vinay Mistry

Chief Commercial Operations Officer
Apollo

Nick Moore

CEO
Argenta Group

Sophie Roberts

Specialist Media Consultant and Former Head of The Insurer TV

Georgina Rothman

Underwriting Portfolio Director
Asta

Rachel Turk

Chief of Performance & Strategy
Lloyd’s

Rachel Webber

Head of Reinsurance UK
Canopius

Sydonie Williams

Head of International Cyber Risks
Beazley

Explore the findings from the 2025 Summit

Explore here

Related content

Indexation for Portfolio Trackers

4th March 2026

Executive summary

This paper examines the use of indexation in the review of portfolio trackers and documents methods to measure it. Currently, there is no universally accepted market standard for measuring indexation. As heightened attention has been placed on the performance of portfolio trackers, it is becoming increasingly important to accurately report and be able to compare indexation across facilities so that targeted action can be focused on areas that require it most. It can also form part of contractual arrangements between a portfolio tracking coverholder and underwriters, enhancing the need for calculations to be accurate and transparent. The following discussion suggests a framework for measuring indexation that could be adopted by underwriters and coverholders if thought appropriate.

What is indexation?

Portfolio trackers are supposed to ‘track’ or ‘index’ an entire portfolio of risks, sometimes across many classes of business. Indexation is a measure used to track how well capacity is being deployed across a portfolio. Indexation can also be used to detect anti-selection, i.e. where capacity is more likely to be deployed behind hard-to-place/lower performing risks as opposed to being deployed across all in scope risks. It is not relevant for coverholders operating other follow facilities in which the aim is something other than to track an identified portfolio of risks.

Indexation is typically reported as a percentage – premium or policy count in respect of bound business divided by that which is in-scope for the portfolio – where perfect indexation is 100% (see below). Other terms commonly used in relation to indexation include ‘utilisation’ and ‘adoption’. While these may sometimes refer to distinct metrics, they are frequently used interchangeably, highlighting the need for clarity, so all parties understand what is being measured and reported.

It may be the coverholder’s responsibility, if set out in the agreement, to gather all the relevant information and data to calculate indexation in an appropriate manner. The accuracy and completeness of this should be an area of audit or due diligence of the underwriter.

Why is it important for portfolio trackers?

Portfolio trackers should, in theory, balance the good with the bad as they should reflect the in-scope portfolio. If capacity is unfavourably deployed towards historically poor performing risks rather than evenly across the portfolio, then the loss ratio of the portfolio tracker may be impacted, affecting the long-term sustainability of the facility. Conversely, portfolio trackers should not tend to be selective toward the best-performing risks, as they aim to track the entirety of a portfolio that supports the needs of brokers to offer capacity to their clients.

Portfolio trackers succeed through diversification of risk, often by class of business and/or geography. The initial assessment of a portfolio tracker opportunity by carriers involves reviewing the composition of an existing portfolio and future development projections. Any large deviations from the anticipated portfolio mix can impact the success and therefore long-term sustainability of the facility. For example, a facility may have been presented as being 65% short tail, 35% long tail, so if indexation in long tail casualty lines is poor, then the carriers’ appetite may no longer be satisfied by the facility.

Furthermore, large deviations from the anticipated portfolio mix will result in inaccurate actuarial pricing: portfolio trackers typically apply a development pattern based on the projected business composition and derive an expected ultimate loss ratio accordingly. Significant deviation from the anticipated portfolio, i.e. indexation at significantly less than 100%, can lead to an inappropriate ‘loss pick’, reducing a carrier’s ability to accurately reserve.

Monitoring how well indexed a portfolio tracker is relies upon coverholders regularly reporting on indexation metrics, so that any areas of concern around low levels of indexation can be identified early, and a proactive approach can be taken to address the concern.

Methods of measuring indexation

As mentioned above, a standard formula for indexation is set out below: a variety of different metrics can be calculated by replacing the X in the formula (e.g. most likely to be premium or number of risks in the potential portfolio).

The fraction denominator within this formula (i.e. the bottom number of the fraction) is the ‘in-scope’ portfolio – getting this right is difficult and critical – and the top line is the portfolio that has been bound.

The process of measuring indexation can be broken down into three main elements: scoping methodology; reporting metrics; and reporting of missed opportunities.

Scoping methodology

Successful indexation measurement relies on the accurate and transparent assessment of in-scoping risks. Underwriters and coverholders have varying approaches as to what is considered in-scope for indexation. Illustrative examples of “outcome reasons” and categorisation for in/out of scope are given below.

In scope (included within the denominator)	Out of scope (excluded from the denominator)
Not taken up due to broker choice	100% single markets
Not offered due to late submission	Unapproved leaders/risk codes
Not offered due to delayed referral response	Policy periods exceed maximum for facility
Not offered due to limited aggregate capacity	Excluded territories/insureds/failed ESG
Client declinature – pricing	Suitable alternative option taken up
Client declinature – insurer continuity/loyalty	Broker lost the account at renewal
Client declinature – outstanding claims	Referral declined
	Limits in excess of facility maximum $/% line size

On the above table, where the parameters of the facility have restricted the placement, this should be deemed ‘out-of-scope’ as there is nothing the coverholder can do to place the business in the facility. ‘In scope’ reasons are those over which the coverholder has some degree of control or influence over the placement or otherwise of the business in the facility.

It may be that it is unclear as to whether a given item should be deemed in or out-of-scope for indexation purposes, e.g. reduced lines/missed opportunities due to limited aggregate capacity. The scoping exercise is typically carried out jointly between the Lead Underwriter and coverholder, and the in-scoping criteria shared with the panel participants for transparency. Following this in-scoping exercise, the quality and range of reporting metrics then add quantifiable results that can be monitored.

The in-scoping process is most effective when it occurs both prior to the facility period and during, by class of business. An initial assessment of the in-scope portfolio is necessary for parties to consider its likely performance. Underwriters should conduct an ‘ex-post’ in-scoping at regular intervals once the true subject portfolio is known with certainty. This allows for unknowns, such as new or lost business or any uncertainty or inaccuracies in the in-scoping process, to be corrected prior to the calculation of the indexation. This is necessary to avoid corrupting indexation measures with irrelevant data.

Reporting metrics

Some coverholders measure indexation by combining multiple metrics to aid high-level tracking over time. However, it should be remembered that there is no single measure that can fully convey the indexation of a portfolio. True indexation requires assessment using a variety of different metrics, as the success of one metric does not necessarily result in good indexation throughout. Examples of metrics that can be used to measure indexation include the following:

% Client Indexation – proportion of in–scope clients that bound at least one policy.
% Policy Indexation – proportion of total in–scope policies (UMRs) that were bound.
% Premium Indexation – proportion of in–scope premium that was bound. This should ideally be measured using GWP for the portfolio tracker’s participation.
% Layer Indexation – proportion of in–scope layers that were bound. This is typically done on a per-programme basis and then averaged across a portfolio.
% Signings – proportion of offered capacity that was signed (written vs. signed line).
Average % of Order (a) – the average signed % line relative to the total order of the policy. Note: relevant for portfolio trackers that offer lines based on order size.
Average Signed % line (b) – the average signed % line. Note: relevant for portfolio trackers that offer lines based on whole only.
Average Limit utilization – the average $ limit deployed across a programme.

The final three metrics listed above monitor the capacity deployment and can be used to assess any areas where capacity is being unduly restricted by the line size or capacity limits (e.g. where the percentage line is restricting the $ use or vice versa).

Policy and premium indexation measures are the most typical measures in use today. The more granular and regular the reporting, the easier it is to identify trends to proactively manage any actions required to improve indexation.

Reporting of missed opportunities

The final element of indexation that may be reported on is in-scope opportunities that were ultimately not taken up. For example, if a portfolio achieves 98% indexation, reviewing the reasons for the remaining 2% through an ‘NTU’ (not taken up) assessment provides context as to why they were not taken up, so these can be addressed to improve indexation.

Although out-of-scope missed opportunities are not directly relevant to indexation monitoring, tracking metrics will increase transparency and identify areas where expanding the facility’s scope may address gaps, such as risks involving unapproved leaders or risk codes. Underwriters and coverholders could also assess the relative performance of the out-of-scope business.

Conclusion

Reporting indexation with multiple metrics by class of business and in total provides a comprehensive picture. Regular reporting allows underwriters to fully assess the performance of the capacity deployment. Coverholders should have the ability to gather all the necessary information and data to calculate indexation in an appropriate manner.

Appendix A illustrates an example indexation report for a specific month across various metrics. The first two columns demonstrate the difference between the initial in-scope EPI prediction for the month relative to the revised EPI following any adjustments to the in-scoping exercise. At a high level this comparison allows recalibration of the indexation figures against the revised EPI, while still reporting on the initial EPI for the month so that carriers are aware of any movements against original projections.

Appendix B then shows an alternative view where a single indexation metric is selected, with variability shown by month per class of business. This allows for visibility of any trends in indexation over time, highlighting any areas of concern in need of review.

Appendix C goes into greater depth around the challenges associated with calculating indexation.

Appendix A – Indexation reporting for each month independently

An illustrative table showing the individual metrics by class of business, with the ability to select a timescale (e.g. monthly, quarterly, annually, year to date). Regular reporting will add context to the indexation of the portfolio by assessing multiple indexation metrics simultaneously.

Select reporting period ▽
	In-scope premium (initial EPI)	Revised In-scope premium	% Client indexation	% Policy indexation	% Premium indexation	% Layer indexation	% Signings	Average % Order/Average signed % line	Average Limit utilisation
International Property
North American Property
Construction
Product Recall
Power
Marine Cargo
Marine Hull
Marine Liabilities
Specie
Energy
International Casualty
US Casualty
Healthcare and Other Casualty
Professions
Commercial D&O
Financial Institutions
Other Financial Services
Cyber
…

In-scope premium (Initial EPI): Based on the initial in-scope analysis, what the estimated premium income (EPI) was

Revised In-scope premium (ultimate EPI): With hindsight, what ultimately ended up being in-scope:

Following the removal of any risks that were no longer in-scope (e.g. policies that moved to 100% single markets)
Following the addition of new business that fits the in-scoping criteria

Appendix B – Indexation reporting demonstrating trends by indexation measure

A second illustrative table showing the indexation measurements over time (per month independently), with a drop-down to select the indexation measure you wish to review, e.g. client indexation, policy indexation, premium indexation etc. This report will give visibility over how indexation is tracking with time per class of business.

Indexation measure ▽
	January	February	March	April	May	June	July	…
International Property
North American Property
Construction
Product Recall
Power
Marine Cargo
Marine Hull
Marine Liabilities
Specie
Energy
International Casualty
US Casualty
Healthcare and Other Casualty
Professions
Commercial D&O
Financial Institutions
Other Financial Services
Cyber
…

Appendix C – Example process and challenges

Example process flow

The process of determining the in-scope portfolio starts with the pipeline of upcoming renewals (and to the extent possible, new business) and assessing their suitability for the tracker against the underwriting parameters.
All risks are appropriately labelled based on the outcome, so that any risks that did not bind have reasons as to why they were not taken up.
These NTU reasons are categorised as either in-scope or out-of-scope. Some risks that previously met the in-scope criteria may not have been eligible for renewal due to changes in the underlying factors, e.g. change in leader that was no longer in scope. Additionally, any business lost by the broker needs to be de-scoped from this exercise.
Retrospectively review any additional risks that were bound that may not have been in the initial in-scope pipeline, including new business won by the broker, or newly in-scope risks that previously did not meet the underwriting parameters.
A full in-scoping review from pipelining stage to post-bind reconciliation is required to fully assess the in-scope criteria against which indexation needs to be measured. This requires there to be consistent labelling of outcome reasons. Parties need to pay attention that confidentiality and competition concerns are not engaged.

In a facility’s first year, there is often insufficient data to form a perfect in-scoping exercise, making the ex-post monthly review of the in-scoping vital in understanding true indexation as opposed to inaccurate in-scoping. Additional controls/sign-offs may be required during this period to prevent significant deviations from expected early on.

Should 100% indexation be achievable?

There will always be optionality for clients as to whether or not to accept capacity from a portfolio tracker, meaning that indexation will never reach 100%. As a result, many trackers now offer incentives to clients, such as discounts on the lead premiums, encouraging the use of the tracker capacity. The end client cannot be forced to purchase cover from tracker portfolios but can be encouraged and incentivised to do so, subject to any duties that the broker will owe to the insured. The coverholder can also take steps such as communicating the benefits of utilising the tracker to both brokers and clients in areas where indexation is poor. Stating the obvious, giving discounts to lead premiums and additionally payments for participating on the tracker will mean that the portfolio will track at a higher loss ratio than an open market lead on the same business.

Should client declinatures count negatively against indexation?

As discussed above, indexation is a measure of how well the coverholder is deploying the capacity. If a client chooses to decline the portfolio tracker’s capacity, should the coverholder be penalised for this, given it is out of the coverholder’s control? Some suggest that client declinatures should be de-scoped and not be included within the denominator. This would support 100% indexation being achievable to the coverholder based on factors that are within their control.

However, this may devalue the indexation metric. Furthermore, clients may be sceptical about the optionality of portfolio trackers should they see that portfolio trackers are achieving 100% indexation, as clients should always have the final decision as to whether they opt to take it up. However, a proposed workaround for this is having separate indexation measures including and excluding client declinatures, so that declinatures outside the control of the coverholder are not taken into account. Overall indexation may be 85%, but excluding client declinatures, coverholder indexation may be much higher, again adding further context to the final metrics.

Should reduced lines following aggregate limit restrictions count negatively against indexation?

Contractual aggregate exposed limits and modelled limits are often set upfront based on the anticipated portfolio, so that underwriters can manage their exposure and purchase appropriate reinsurance. Coverholders monitor utilisation relative to these limits, and if they are nearing these limits, they may cut back on line sizes or decline risks in some regions. If the coverholder was not proactive in assessing changes in the portfolio throughout the year and addressing any requests to increase limits in advance of any concerns, then any remedial action of reducing line sizes/declining risks may count negatively on indexation.

If, however, a small limit was deliberately set by underwriters due to appetite, then any reduction in capacity as this limit is neared may not count against the coverholder from an indexation perspective (though the line size should have been reduced for that class/region to manage this in a controlled way).

How should premium indexation be calculated?

Ideally, premium indexation should be measured using the GWP for the portfolio trackers’ participation. For large programme limits, the % line sizes will often be restricted by the $ limits, and the in-scope premium for the portfolio tracker will depend on the programme structure. What is deemed the in-scope GWP to the portfolio tracker can therefore change significantly from the pipeline stage until the risk(s) have bound, as policies become in/out of scope and premiums are negotiated. For NTUd risks, the only way to determine this in-scope GWP would be to quote the portfolio tracker lines retrospectively based on the bound policies, which would often be a manual exercise and difficult to regularly review.

Where premium indexation is presented using 100% GWP for the in-scope policies, rather than calculating the portfolio trackers’ participation based on realistic %/$ lines, this does not truly reflect the premium indexation across a portfolio as the outcome would result in very high premium programmes unfairly skewing the premium indexation results even if the tracker line sizes in scope were small.

Acknowledgements

The LMA would like to thank the members of the CUO Committee and the CUOC Enhanced Underwriting Subcommittee for their contribution to this report. Thank you in particular to Colum D’Auria and Elinor Taylor.

Related content

Explore the committee

Chief Underwriting Committee