🤖 How Underwriting and Claims Are Reshaped by AI — And How They Stay the Same

A concise, practical overview of current AI use in underwriting and claims: intelligent document processing and LLM-driven summarization, risk stratification and triage, and limited straight-through processing for simple cases - paired with discussion of bias, governance, and evolving regulatory expectations.

🔗 Risk & Insurance

🧾 What Brokers Can Learn From Insurer Data — And What They Shouldn’t Ignore

A broker-oriented write-up that translates insurer data practices into operational metrics brokers can use (profitability, growth, productivity), highlights MGAs as examples for speed and sector focus, and stresses culture-first adoption of technology.

🔗 Insurance Business

Over the past few years, the conversation around artificial intelligence in insurance has shifted. It’s no longer about whether AI can help with underwriting, claims, or distribution. That’s already happening. The real question is what this means for the people working in the industry. Most experts don’t believe automation will simply wipe out jobs. Instead, it’s changing them, sometimes in subtle ways, and forcing companies to think differently about the skills their employees will need.

Take underwriting and claims, for example. Software is now perfectly capable of handling routine work like scanning documents, pulling out key data points, or directing files to the right department. That frees up time but also reshapes the role itself. Instead of being buried in paperwork, underwriters and adjusters are more often expected to interpret what the systems produce, make judgment calls in complicated cases, and ensure the process holds up under regulatory and ethical scrutiny. In other words, the work is moving away from pure transaction handling and closer to higher-level decision-making.

This shift also puts new kinds of talent in demand. Data scientists, AI specialists, and people who can build or oversee these systems are becoming just as central to an insurer’s success as actuaries have traditionally been. To keep up, many carriers are experimenting with reskilling programs or forming partnerships with universities and tech firms to bring in fresh expertise.

But the real challenge may not be technical at all. Insurance has always attracted professionals who care about risk, trust, and long-term stability. The companies that succeed in the next decade will be those that find a way to combine that mindset with genuine comfort around data and digital tools. Striking that balance - human judgment paired with machine intelligence - could be what separates the leaders from the laggards in the years ahead.

🌪️ Aon Launches Analytics Platform for Catastrophe Response

Aon has introduced a new analytics platform intended to support catastrophe response and modelling, positioning analytics as a tool to improve immediate response and client guidance after major events.

🔗 Business Insurance

🧠 CLARA Analytics Launches First Intelligence-as-a-Service

CLARA announced an “Intelligence-as-a-Service” for claims leaders that leverages its large, normalized claims dataset to deliver industry benchmarking, reserving insights and configurable analytics aimed at improving reserving accuracy and claims outcomes.

🔗 Financial IT / CLARA Analytics

📊 AI Surpasses Inflation as Top Priority for Global Insurance Executives in 2025

The International Insurance Society’s 2025 Global Priorities Survey reports that AI has become the single most important industry priority—surpassing inflation—with two-thirds of surveyed executives citing AI as their leading technology and innovation focus.

🔗 Business Wire / International Insurance Society