🤔 Will AI need its own insurance policy?

This article explores how the rise of AI in business may prompt the insurance industry to create dedicated AI policies, much like the evolution of cyber insurance. As adoption grows and traditional coverages prove insufficient, industry leaders suggest insurers may need new underwriting models and clearer policy language to address emerging AI risks.

🔗 Insurance Business

💰 Insurance companies are embracing AI. But they aren’t talking much about ROI

A more in-depth look at the AI Index for Insurance from Evident, that we also covered in the last issue. Beyond rankings, the article highlights how insurers are navigating the challenges of adopting AI responsibly - balancing innovation with regulatory compliance and transparency. It also notes that AI leadership is increasingly tied to talent acquisition, ethical deployment, and internal governance structures, not just technological capability.

🔗 Fortune

🌀 Data without direction is just noise

Interesting blog post about the risks of collecting insurance data without clear objectives. Matt Scott argues that more data and advanced AI tools are not inherently valuable unless they serve a defined purpose—and warns that hyper-personalization may lead to ethical challenges like discrimination and the exclusion of vulnerable groups.

🔗 Insurance Times

🔥 Beyond Prediction: Wildfire Mitigation That Works

In the newest episode from the Predict & Prevent podcast, Pat Blandford, the CEO and founder of Green Shield Risk Solutions, talks about their wildfire analytics and prevention platform Property Guardian, which combines advanced propagation modeling, vegetation data, and mitigation-focused insights to help insurers, property owners, and communities predict risk and proactively harden properties against wildfire exposure.

🔗 Predict&Prevent

Applying for homeowner insurance can be a tedious process. A typical application form requires prospective customers to enter a wide range of information about their property. Apart from the address, these usually include details such as:

Even if the process is digital (hopefully it is!), filling out all these fields is time-consuming and annoying for the prospective customer. From the insurer’s perspective, manual data entry means increased friction, more errors and potential drop-offs before a quote is ever generated.

So, how can data analytics help streamline this process?

Imagine what a seamless customer experience would look like: you enter your home address into the insurer’s website, and voilà: all relevant property data is automatically filled in. All you need to do is review the information and confirm with the click of a button. Fast, easy, and frictionless.

This is not just a futuristic vision. It’s already possible in several countries.

Governments around the world are increasingly embracing Open Government Data (OGD) policies. Under these initiatives, governments publish geospatial and infrastructural data about buildings, often freely and publicly available. For insurers, this presents a valuable opportunity.

Take Germany as an example: The Federal Agency for Cartography and Geodesy provides so called LoD2 (Level of Detail 2) building data to authorized users. These data sets include 3D models of buildings enriched with useful metadata such as:

With this free information alone, insurers can pre-fill certain underwriting criteria for leads in the quote process. Even better: combining LoD2 and satellite imagery, insurers could estimate living space, detect the presence of solar panels, or assess potential risks related to neighboring properties.

And Germany is not alone. Many other countries - from the Netherlands to Canada to Singapore - are publishing similar datasets, enabling insurers and other industries to build smarter, data-driven processes.

While Open Government Data is a great starting point and a great way to showcase the value of geospatial data to senior stakeholders, it can be inconsistent, outdated, or limited depending on the country or region where you operate. This is where private data vendors come into play.

Several companies specialize in aggregating, cleaning, and enriching property data at a national or even global scale. These vendors usually offer APIs that allow insurers to pull:

By combining public and private data sources, insurers can offer near-real-time quotes based solely on an address input. This reduces form abandonment, speeds up onboarding, and enables a smoother, more modern customer journey.

📞 Voice AI company SuperDial picks up $15M series A to automate insurance calls

SuperDial, a startup automating insurer phone calls with voice AI agents, has raised $15 million in Series A funding led by SignalFire. Originally launched as a billing platform, the company now streamlines high-friction tasks like benefits verification and claim follow-ups - promising up to 4x productivity gains for provider billing teams.

🔗 Fierce Healthcare

🤖 Aon Launches AI-Powered Aon Broker Copilot for Commercial Insurance

Aon has unveiled its new Aon Broker Copilot - an AI-driven platform built on large language models and predictive analytics to enhance commercial insurance placement. By capturing structured data from every submission (quoted, bound, or declined) the tool delivers real-time insights on pricing, carrier appetite, and market sentiment, empowering brokers to offer sharper, more informed advice.

🔗 Insurance Journal

🦾 MetLife teams up with Sprout.ai to drive claims automation

MetLife has deepened its partnership with insurtech firm Sprout.ai, rolling out advanced AI-powered claims automation across its US, Asia, and Latin American operations. The integration blends Sprout.ai’s NLP and OCR technology with MetLife’s existing systems to speed up processing, boost accuracy, and enhance the overall customer experience.

🔗 Life Insurance International

🩹 YuLife unveils Preventative AI to cut insurance claims

UK InsurTech YuLife has unveiled Preventative AI, a new underwriting solution that uses biometric data, machine learning, and explainable AI to shift risk assessment from reactive to proactive. By leveraging real-time data from wearables and behavioral insights, the system enables dynamic underwriting, early intervention, and reduced claims costs -offering a data-driven path toward healthier policyholders and more sustainable insurance models.

🔗 FinTech Global