As the insurance industry accelerates its digital transformation, artificial intelligence has emerged as a powerful enabler. Tools like ChatGPT and Microsoft Copilot have captured attention for their ability to generate text, summarise content, and support productivity. However, when applied to high-stakes, highly regulated insurance processes, these generative AI models face severe limitations.
For insurers, precision, explainability, and compliance is non-negotiable. That’s where hybrid AI comes into play—offering a more reliable, transparent, and domain-aware alternative to black-box generative tools.
he large language models (LLMs) behind ChatGPT, Copilot and Gemini analyse language to generate a fluent, human-like response. But they are processing, not thinking, and fluency doesn’t guarantee accuracy.
In insurance, that is a real problem. Generative AI can:
Generative AI in workflows like underwriting, claims handling, or fraud detection, where decisions must be explainable, verifiable, and correct.
Hybrid AI blends symbolic AI (which leverages rules, taxonomies, and knowledge graphs) with machine learning, delivering safe and transparent decision-making results. expert.ai Hybrid AI for Instance delivers the best of different types of Artificial Intelligence without sacrificing rigour and adaptability. Importantly, it’s designed to handle the complexity and compliance needs of underwriting, claims and customer service.
Here are just a few examples of how expert.ai has delivered value using hybrid AI to insurers and carriers:
Hybrid AI reads incoming emails, PDFs, and handwritten forms to understand the claim’s context, accurately classify it, and route it with key information highlighted to the right team, saving time and reducing manual effort.
Using insurance-specific taxonomies and internal guidelines and policies, hybrid AI assesses whether policy documents meet compliance standards and flags gaps to ensure alignment with evolving regulations and minimise risks.
Hybrid AI identifies and links key entities across documents (e.g., claimant, broker, accident details) and detects noncomplying claims or inconsistencies that may signal fraudulent intent, along with the reasoning behind the result.
detects signals that indicate sentiment, urgency, and the need for escalation in customer interactions. This allows faster more satisfying service for customers and is especially useful for employees seeking to understand the context and the customer’s request.
Highly complex documents—like policy schedules, endorsements, claims slips and reinsurance contracts—are transformed into structured information essential for downstream analytics, automation and compliance auditing.
While hybrid AI is ideal for mission-critical and regulated processes, it can also be used in Generative AI, with lower risks than generalised GPT like Copilot ChatGPT or Gemini. Generative AI solutions are ideal for internal use cases such as:
A hybrid approach enforces guardrails and data protection policies and, most importantly, helps maintain well-deserved trust in your organisation.
Launch your AI initiatives in weeks, not months. Proven pre-built solutions significantly reduce the time and resources needed to get up and running.
Hybrid AI technology, combining symbolic and machine learning approaches, ensures a deeper, more accurate understanding of your data from the start.
While our solutions are ready to go, they are not black boxes. They provide a solid foundation that can be easily customised to fit your unique business processes and data, giving you the best of both worlds.
