This piece is by Pavlo Khropatyy, VP, Global Head of Delivery FS&I, Intellias.
Pavlo is the Director of Engineering and a Head of Fintech segment of Intellias, building fully-fledged strategic partnerships with clients across the UAE, the USA, and EU markets.
An industry practitioner of 18 years, Pavlo is implementing a highly effective technology consulting, business and operations IT strategy for the finance domain promoting the companies’ growth into organisations with technology at heart.
Artificial intelligence (AI) is transforming the insurance industry. Tasks once handled exclusively by human experts – from resolving claims to engaging customers and assessing risk – can now be automated by intelligent software agents. These AI agents for insurance work quietly in the background, completing essential tasks at speed and scale, and without the friction of manual processes. Adoption of this powerful technology is accelerating. In 2024, 75% of health insurance providers used AI for customer service, and 50% deployed it to enhance claims management. The appeal is clear – AI agents can handle more work, faster, and more accurately, all while reducing costs.
What are AI agents in insurance?
AI agents are intelligent software systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike reactive tools like ChatGPT, which require user input, AI agents are proactive. They can operate independently, making decisions based on real-time data and long-term objectives.
In insurance, these agents are designed to tackle the sector’s unique challenges. They combine machine learning (ML), natural language understanding (NLU), and generative AI to automate and enhance processes such as: customer interactions via chatbots and virtual agents, fraud detection by spotting suspicious claims early, and regulatory compliance through automated monitoring.
Because AI agents can handle multiple tasks simultaneously around the clock, they enable providers to scale operations while maintaining accuracy and consistency. McKinsey states that AI adoption maturity directly correlates with personalisation – and ultimately, customer value.
How insurance providers are leveraging AI agents
AI agents are reshaping every part of the insurance value chain. Here are just three areas where they are delivering significant impact:
1. Claims processing and fraud detection: AI agents can automate claim assessments, reducing processing times from weeks to hours. They can also flag suspicious activity in real time, helping insurers prevent fraud before it hits the bottom line – a major win in an industry that loses hundreds of billions to fraud annually.
2. Underwriting and risk assessment: By analysing dynamic data sources, AI agents produce more accurate risk profiles than traditional static methods. Predictive analytics allows insurers to anticipate potential risks before they occur, improving pricing accuracy and profitability. Deloitte predicts the AI underwriting market could grow from $40 million in 2024 to $4.7 billion by 2032.
3. Customer service and engagement: Conversational AI enables instant responses to queries around the clock. More advanced agents can tailor responses to individual customers based on their history and preferences, increasing satisfaction while reducing operational overheads.
Other fast-growing use cases include regulatory compliance, customer onboarding and verification, document management, telematics-based pricing, and even product innovation and market intelligence. In all cases, AI agents are not just making processes faster – they are making them smarter.
Any insurer that writes off AI agents as a passing tech fad is setting itself up to fall behind. AI agents offer tangible benefits that directly impact competitiveness, profitability, and customer loyalty. And like every major technological shift before it, the rise of AI won’t wait – for insurers, ignoring it would be a costly mistake.
Building LLM-based AI agents for insurance
Large Language Model (LLM)-based AI agents are especially powerful for insurers. Their ability to interpret complex queries and respond with human-like communication makes them ideal for customer service, policy guidance, and risk assessment. It’s no secret that building one is complex, and takes thorough research and stringent planning. There are four key phases to work through:
1. Planning and preparation: Define goals, select a suitable LLM, collect and clean relevant data, and choose a development platform.
2. Development and design: Train the model on domain-specific data, build the agent architecture and integrations, implement natural language understanding (NLU), and ensure outputs match brand tone and clarity.
3. Ethics and usability: Mitigate bias, design an intuitive user interface (UX), and prioritise transparency and explainability.
4. Testing, deployment and continuous improvement: Stress-test performance and security, launch at scale, continuously retrain on new data, and provide staff training and documentation.
Building for the realities of modern insurance
The industry is entering its “Open Banking” moment, where Application Programming Interfaces (APIs), interoperability and user-first design are becoming foundational. To realise the true potential of AI, insurance providers should pay attention to several key priorities.
1. Bespoke and niche brokers: Niche brokers work in complex markets like fine art, marine cargo or Small and Medium-sized Business (SMB) bundles. They need lightweight, flexible tools that are quick to adopt. API-first ecosystems using Representational State Transfer
(REST) or Graph Query Language (GraphQL), with low-code connectors into Customer Relationship Management (CRM) and legacy systems, are key. This is where user experience (UX) is central – dashboards, co-browsing, e-signatures and embedded payments should enhance human interactions rather than replace them. Even gamified elements like micro-rewards can boost engagement.
2. Plugging into MGA platforms and pricing engines: Interoperability is critical. Many brokers juggle multiple Managing General Agent (MGA) portals, each with its own interface. With that in mind, AI agents should support standard integration patterns for systems like Guidewire, Duck Creek, Majesco or Sapiens, while embracing new API-first MGAs. Standards like Association for Cooperative Operations Research and Development (ACORD), Open Insurance APIs and Lloyd’s Blueprint Two are making “connect once, use many” possible.
Real-time event streaming ensures everyone sees the same risk and pricing data. Whilst data enrichment from geospatial, Internet of Things (IoT) or cyber sources can further improve underwriting accuracy.
3. Automated compliance via AI: Compliance spans Know Your Customer (KYC), Anti-Money Laundering (AML), General Data Protection Regulation (GDPR), sanctions checks and audit trails, and AI has the capability to automate this from end to end. LLMs can review documents for missing clauses or unfair terms, anomaly detection can flag suspicious premium flows or claims, and AI can also auto-generate audit trails and regulator-ready summaries – with explainability built in to meet frameworks like the EU AI Act.
Prioritising AI gives insurers a competitive edge
AI agents are reshaping insurance – making processes faster, more accurate and more customer-centric. As the industry embraces open ecosystems and real-time data flows, building effective AI agents will be a strategic necessity, not a nice-to-have. Those who design for interoperability, usability and compliance from day one will be best placed to lead the next era of insurance innovation.
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