On 21 January we ran a webinar called Insurance Claims Policy Checking 2025: Bottlenecks, benchmarks & breakthroughs.
We received many more questions than we could answer live, so our expert speakers have addressed them here. For more information please contact us or request a demo.
Which policies or product lines are best to start with when applying AI to coverage checking?
Most organizations see the fastest results by starting where manual effort is highest and volume is meaningful. High-volume, lower-value claims with relatively standardized coverage are often a practical entry point, particularly where handlers spend significant time reading unstructured documentation.
That said, AI does not need to be limited to full automation. For more complex policies, many firms begin by using AI to support handlers, surfacing relevant clauses, endorsements, and limits, rather than making final decisions. This builds trust while still delivering measurable efficiency and consistency gains.
Can AI at FNOL really handle ambiguity in commercial policies? How does this feed back into underwriting?
Ambiguity in commercial policies often exists before a claim is notified. Leading insurers address this by strengthening the feedback loop between claims, underwriting, and legal teams. Where so many elements of insurers’ processes are manual, it is very difficult to revert to underwriters with feedback.
Where claims data and coverage outcomes are captured systematically, insurers can use AI to identify recurring areas of ambiguity and automatically feed that insight back into policy construction. Over time, this reduces unintended claims outcomes and improves clarity at inception. Agentic AI can be used to keep in-house legal input on coverage, or the need for regular dialogue between handlers and underwriters, to a minimum.
How do regional variations or broker-specific wordings complicate automation?
The overall methodology for using AI to assist policy coverage decisioning processes is fully transferrable between regions, brokers, carriers, etc. However, the AI does need to support the language of the country and be trained on the relevant coverage type and adjudication process.
Multi-language accuracy can be challenging to sustain with internal buildouts, and it is important to verify language support before engaging external technology providers. At Sprout.ai, with customers located throughout North America, Latin America, Europe and Asia Pacific, we have trained our AI to be fully multi-language with no loss of accuracy – even in Japan!
Will we ever achieve fully automated policy checking for some coverages?
In some areas, we already have. For standardized products with deterministic decision logic, full AI-driven automation is achievable today.
For more complex coverages, AI is typically used to assist handlers rather than replace their involvement at the policy coverage checking stage. However, as confidence, explainability, and governance mature, the scope of automation will continue to expand. Trust, not technology, is usually the guiding factor.
Does increased regulatory scrutiny create friction for AI-assisted policy coverage decision making?
In practice, regulation is rarely the main blocker. Most regulators are creating frameworks focused on outcomes, accountability, and risk management, rather than prescribing how AI should be used.
Often, it is internal risk aversion, not regulatory mandate, that imposes stringent rules that slow progress. Well-designed AI systems with strong audit trails and human oversight can actually be more compliant and transparent than manual processes, where many errors may originate from rekeying, misinterpretation, misreading, or lack of clarity. AI moves us towards an always-on QA environment, which is a huge step forward for compliance.
What documentation or evidence do regulators expect when AI helps validate coverage?
Regulation differs from country to country, and interpretation of regulations also varies. As a general rule, auditability and traceability of every policy coverage decision, and evidence of the reasoning, should exist. Sprout.ai provides a defensible audit trail of the reasoning for every coverage decision, including granular references to relevant policy wording.
How can insurers overcome the complexity of integrating AI with policy and claims systems?
Integration can be a real challenge, but our approach at Sprout.ai is to build modular solutions that use APIs to integrate with the systems we co-exist with. We have experience of integrating pretty seamlessly with both commercial and homegrown policy and claims systems, with minimal friction, and no disruption.
Is poor availability of real-world data a blocker to training and validating AI models for insurance claims?
High-quality, real-world data is important – and difficult to obtain. Training AI models using real-world, insurance-specific data is essential when the goal is to maximize accuracy.
At Sprout.ai we use real claims data whenever possible, in combination with synthetic data. Over the past 8 years we have had access to a huge amount of sector-specific, real-world data, which is undoubtedly the reason we are able to achieve such high levels of accuracy at speed and scale.
Does AI work best when policies are clear, or when they’re complex and ambiguous?
Clear policies benefit everyone, the customers, handlers, and AI alike. If a skilled handler cannot confidently interpret a policy, AI will also find this challenging.
That said, AI delivers particular value in managing complexity consistently, surfacing ambiguity, highlighting conflicts between wordings, and supporting better decisions early in the process. However, improving policy construction and using AI downstream are complementary, not competing, strategies.
A large proportion of claims handling is delegated by carriers. How does that work?
While there are nuances between the operational structures of carriers compared to TPAs and MGAs, any organization that handles claims, needs to adjudicate them and determine policy coverage, would benefit from AI-driven automations.
Sprout.ai is working with each of these organization types, implementing automation and AI-assisted support where there is claims delegated authority. The value realized by these firms tracks to the same value drivers attained by carriers: efficiency gains, operational cost savings, and service improvements.
Can GenAI help solve complaints faster?
Human error is very often cited as an underlying factor in complaints. AI-assisted decisioning processes help to reduce the errors that lead to complaints, through standardization, automation and the provision of transparent and auditable processes for the determination of coverage.
AI can also help insurers to handle complaints effectively, by directing them to the people who can respond to them quickly, fairly, and with empathy.
We’re moving towards an always-on QA environment, which is a significant step forward in reducing complaints and resolving them in a fast and fair manner.
Is AI replacing the claims handler’s role in coverage validation?
No. Across the panel, the consensus was clear: AI supports judgement, it does not replace it. The work of the handler, who remains accountable for outcomes, is augmented by AI, not replaced.
AI’s role is to reduce cognitive load, improve consistency, and surface evidence more effectively – so handlers can spend more time progressing claims – especially those that are complex in nature – than on mundane work.
How do you bring claims handlers onside when there are concerns about job impact?
Involving handlers early is critical. Adoption is significantly higher when AI is positioned and designed as decision support rather than replacement, and when it demonstrably removes low-value, repetitive work. Engaging the people who live the problem every day and involving them in shaping the solution builds ownership from the outset.
It is also important to identify and empower a small number of trusted change agents within the claims handler community. These individuals can help shape the solution, act as advocates, and translate the value of AI into day-to-day practice for their peers, helping to build credibility and drive adoption organically.
Trust grows when handlers can clearly understand how and why AI reaches a conclusion, and when they are able to challenge or override it where appropriate. By combining transparency, handler input, and internal champions, AI becomes a tool that supports talent shortages, improves efficiency, and enhances decision-making, rather than something done to claims teams.
Where else will AI have the biggest impact in claims over the next few years?
Beyond coverage checking, the experts highlighted AI-driven claims triage, quality assurance, complaints handling, and post-settlement analysis as key growth areas. Across all of these, the common theme is consistency, transparency, and scalability.
However, FNOL coverage determination is not just about speed – it is about handling complexity early. While real-time validation for straightforward claims is improving, and zero-touch claims will have a significant impact, the greatest operational and financial gains will come from extending early AI-driven coverage clarity into complex, multi-policy scenarios.
Follow or connect with the speakers on LinkedIn:
Ian Thompson, Global Insurance & Claims Expert, IMT Advisory