The London Market insures the world's most complex and unconventional risks, many of which lie beyond the reach of standard underwriting. Yet, when it comes to leveraging Artificial Intelligence (AI) to eliminate operational inefficiencies and unlock new strategic advantages, the market trails its global peers.
According to recent research, 47 percent of London Market firms are “experimenting with AI tools, but without wide adoption.” In underwriting processes, a mere 14 percent have employed Generative AI (Gen AI) or Agentic AI , while 12 percent have no plans to use these technologies.
This gap represents a powerful opportunity for enterprises. Take, for instance, a leading specialty and commercial insurer who struggled with a slow underwriting process and limited risk visibility because of long research cycles and siloed data. An Agentic AI-powered research assistant reduced research time by 85 percent and research costs by 92 percent. On the claims side, Gen AI is enabling companies to resolve 70 percent of simple claims in real-time while delivering better customer experience.
Such results demonstrate the significant competitive edge that AI can deliver when deployed at scale – not just tested in isolation.
AI Use Cases Across the Insurance Lifecycle
A McKinsey study found that insurance companies that successfully embraced AI delivered 6.1x more in total shareholder returns than AI laggards. Best-in-class insurers leveraging AI for domain-specific improvements have unlocked faster new-agent ramp-up, higher sales conversions, premium growth, reduced onboarding costs and improved claims accuracy. AI productivity in business functions is expected to account for USD 50-70 Billion of the industry’s revenue.
Strategic advantage in insurance will increasingly stem from how effectively AI is embedded across the lifecycle.
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1. Pre-Bind Phase
Streamlining Data and Proactive Risk Management
Underwriters still spend hours manually reviewing broker submissions, extracting data from forms, reconciling information across multiple tools and chasing missing inputs. Agentic AI can automate data extraction, validate information using diverse sources and assess eligibility in a matter of minutes rather than days.
Another powerful use case lies in proactive risk management. Agentic AI can integrate cyber risk scoring into the underwriting process by proactively scanning client systems, scoring vulnerabilities and flagging gaps to ensure relevant coverage and accurate pricing.
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2. Quote Phase
Accelerating Precision and Competitiveness
Calculating premiums takes a significant portion of underwriters’ time as it involves manually retrieving historical data from different systems, adjusting for market conditions and building quotes from scratch. This tedious, error-prone process slows response times, affecting broker satisfaction and competitiveness.
Gen AI transforms this stage by creating personalized, data-driven quotes that blend traditional and alternative data sources. Gen AI tools analyze historical performance, market trends and individual risk factors to enhance pricing accuracy and efficiency. This allows underwriters to focus on strategy and relationship management rather than manual computation.
Insurers leveraging AI for pricing and risk management can expect a 10.7 percent increase in Return on Equity (ROE) worldwide. Faster turnaround times – from days to hours – and agility in adapting to real-time market changes offer a decisive edge in the London Market where speed often defines success.
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3. Bind Phase
Automating Workflows and Ensuring Compliance
The binding process involves multiple manual approvals, document validations, policy consistency checks and compliance monitoring, particularly for high-value submissions. These administrative dependencies often create bottlenecks in policy issuance.
Agentic AI autonomously manages submission workflows, prioritizing high-value cases while performing document validations and compliance checks. Unlike traditional rule-based automation, it adapts dynamically to new situations, ensuring accuracy while freeing human experts for higher-value tasks.
Nearly one-third of London Market firms recognize AI’s potential to automate policy review and endorsement. McKinsey notes that insurers using AI at this stage can save up to 40 percent of onboarding costs by streamlining operations.
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4. Post-Bind Phase
Driving Efficiency and Value Recovery
Suppliers handle most operational tasks at this stage, from endorsements to policy administration. A BCG report shows that holistic AI deployment can enable real-time resolution of ~70 percent of simple claims and cut costs by 30-50 percent. Smart technologies such as intelligent indexing, adaptive routing and recovery management play a central role in delivering such efficiencies.
By automating routine communications and claims indexing, AI enhances operational efficiency and allows suppliers to focus on the core business, while underwriters and brokers concentrate on client strategy. AI also uncovers recovery opportunities that might otherwise be missed, converting potential losses into recovered value.
How to Make AI Stick with Change Management
Although the insurance industry leads in AI experimentation, only seven percent of organizations have scaled their AI investments so far and nearly two-thirds are stuck in pilot implementations. The biggest hurdles are not technical but cultural.
Limited business engagement, unclear roles, inconsistent support and the probabilistic nature of AI often clash with the deterministic nature of insurance. Even the most advanced AI system can underperform if people resist change.
Achieving strategic adoption demands a structured change management approach.
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Demonstrate Tangible Benefits Early
Start with small pilots, scale proven successes and embed AI into the daily workflow.
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Invest in Education and Communication
Share insights through whitepapers, workshops and regular updates to build confidence and reduce cultural friction.
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Define Clear Roles and Responsibilities
Establish who monitors AI augmentation and how outcomes will be measured, ensuring accountability from the start.
In the highly regulated London Market, governance and trust are paramount. Model transparency, explainability and proactive bias mitigation are essential to building long-term confidence in AI.
Seize the AI Advantage
The London Market has reached an inflection point. Specialty insurers now have the opportunity to move from small-scale trials and apply Gen AI and Agentic AI more broadly across underwriting and claims. As the global insurance market soars, those who use AI to drive productivity, precision and superior client experience will lead the way.
By selecting high-impact use cases, achieving quick wins and scaling these through structured change management, London Market participants can capture the early adopter advantage. Underwriters should underwrite, brokers should advise and suppliers should focus on the core business. While humans focus on value creation, let AI manage the routine.
The future will favor insurers who combine bold experimentation with disciplined execution.
Talk to our experts to explore how WNS can co-create and scale AI-powered transformation across your London Market operations.