The Risk Intelligence Gap: How Exposure Data Deficiency Is Reshaping Property Underwriting

The Risk Intelligence Gap: How Exposure Data Deficiency Is Reshaping Property Underwriting

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Welcome to Scouting for Growth.

93%.

That is the proportion of UK commercial properties insured for the wrong amount. In the United States, 90% of commercial buildings carry inadequate coverage. And underwriters rate their access to risk intelligence at just 3 to 5 out of 10—at the very moment decisions are made.

This is not a data shortage.

This is a risk intelligence failure.

Data exists across the insurance ecosystem. But it doesn’t reach underwriters in a verified, structured, decision-ready format. What we’re facing is an architecture problem, an integration problem—and fundamentally, a trust problem.

This is the risk intelligence gap, and it is costing the insurance industry billions.

Today, I’m joined by Anthony Peake, CEO of Intelligent AI, a company building API-first property risk intelligence infrastructure—delivering over 100 structured data points per building directly into underwriting workflows.

Why does this matter?

Because in 2024, global insured catastrophic losses reached $145 billion. The US P&C industry posted consecutive underwriting losses exceeding $20 billion. And yet, insurers continue to invest in advanced AI models—while feeding them unreliable, incomplete, and outdated data.

Better engines. Worse fuel.

Let’s fix that.

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🎙️ Conversation Begins

Sabine:
Anthony, you’ve spent over 30 years working with companies like Apple, GE, Oracle, and leading insurance systems. Why focus on property data for commercial insurance?

Anthony Peake:
Because insurance has one of the biggest data problems—and therefore one of the biggest opportunities.

Despite all the innovation in AI and modeling, the underlying data remains poor. And that’s where the real transformation needs to happen.

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🔍 The Automation Paradox in Insurance

Sabine:
We’ve seen insurers invest millions into AI pricing engines and catastrophe models. Yet the data feeding these systems is rated just 3 to 5 out of 10. Why?

Anthony:
Because the industry has learned to live with poor data.

Addresses are wrong. Property details are incomplete. Brokers pass along weak information. And underwriters compensate by manually gathering missing data—sometimes even measuring buildings using Google Earth.

It’s inefficient. It’s not scalable. And it’s not fit for modern underwriting.

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🧠 Understanding the COPE Model in Property Underwriting

Sabine:
Let’s break down COPE—Construction, Occupancy, Protection, Environment. Why is it so critical?

Anthony:
COPE is the foundation of property risk assessment.

* Construction: What is the building made of? Brick, steel, wood?
* Occupancy: What happens inside? Office, factory, chemical lab?
* Protection: Fire alarms, sprinklers, distance to fire services
* Environment: Flood risk, nearby hazards, natural catastrophes

Each building requires over 100 data points to assess risk properly. But most insurers don’t have that data—at scale or in structured form.

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⏱️ The Hidden Data Tax in Insurance

Sabine:
We found that underwriters spend 50–55% of their time chasing and validating data. That’s a massive inefficiency.

Anthony:
It is. And it translates into over $160 billion in lost productivity across the industry over five years.

We’ve seen insurers required to survey 10,000 properties annually. With digital intelligence, we reduced that to 3,000 physical visits—freeing teams to focus on actual risk mitigation instead of admin.

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💸 The Underinsurance Crisis

Sabine:
Let’s talk about underinsurance. Who is actually paying the price?

Anthony:
Historically, the customer.

Many portfolios are only partially revalued each year. Meanwhile, construction costs can rise by 20% annually. The result?

Globally, 80% of properties are underinsured by at least 50%.

But regulation is changing. In the UK, the FCA’s Consumer Duty Act now requires insurers to prove they’ve provided accurate valuations—or pay the full claim.

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📊 Real Example: A Billion-Dollar Gap

Anthony:
We analyzed a portfolio of 355 commercial properties.

* Insured value: £5 billion
* Actual required coverage: £6.17 billion

That’s a £1.17 billion exposure gap—on just one portfolio.

And the insurer was undercharging premiums by millions.

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🧬 Digital Twins in Insurance

Sabine:
You’re building digital twins of properties. What does that mean?

Anthony:
A digital twin is a virtual model of a property using 100 to 300 structured data points.

It allows insurers to simulate risk:

* Fire exposure
* Flood probability
* Business interruption impact

Instead of manually collecting data, insurers can assess risk instantly—and at scale.

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⚡ Speed as a Competitive Advantage

Sabine:
Speed matters in underwriting.

Anthony:
Absolutely.

Some insurers take weeks to assess a portfolio. Others can do it in minutes.

The faster you respond, the more business you win. Data isn’t just about accuracy—it’s about velocity.

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🤖 Building Trust in AI-Driven Insurance

Sabine:
Underwriters won’t act on data they can’t explain. How do you build trust?

Anthony:
By being transparent.

We provide:

* Data sources
* Collection dates
* Accuracy scores

Not all data is perfect. But if underwriters understand confidence levels, they can make informed decisions.

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🚨 Case Study: $300 Million Loss

Anthony:
We analyzed a site insured for $85 million.

In reality:

* It was larger than reported
* Had higher risk operations
* Was partially unprotected
* Had prior flood incidents

Actual exposure? Over $250 million.

That’s the cost of incomplete risk intelligence.

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🌍 Scaling Risk Intelligence Globally

Sabine:
You’re expanding into the US market.

Anthony:
Yes—150 million properties.

The data exists, but it’s unstructured—often locked in PDFs. Our role is to extract, validate, and deliver it via APIs into underwriting systems.

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🔮 The Future: Predict and Prevent

Sabine:
Where is the industry heading by 2030?

Anthony:
From repair and replace to predict and prevent.

* Real-time data
* AI augmentation
* Faster decisions
* Better risk selection

Insurance becomes proactive—not reactive.

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🔗 The Risk API Revolution

Anthony:
We’ve launched a Risk API—delivering structured property data directly into underwriting systems.

We’ve also partnered with Guidewire, giving access to over 540 insurers globally.

This is about embedding intelligence where decisions happen.

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🎯 Final Thoughts

Sabine:
If a Chief Underwriting Officer is listening—what should they do next?

Anthony:
Revisit the “too hard” problems.

The technology now exists. The data exists. The opportunity is here.

You’re making billion-dollar decisions on thousand-dollar data.

That has to change.

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🔚 Closing

Sabine:
The risk intelligence gap is not a future issue.

It is a present reality—hidden inside every portfolio built on incomplete data.

The question is simple:

Who will move first—from data to intelligence?

Because those who do won’t just improve loss ratios.

They will own the best risk.

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If this conversation challenged your thinking, download our research paper The Risk Intelligence Gap in the show notes.

I’m Sabine VanderLinden, and this is Scouting for Growth—where we don’t just talk about the future, we design for it.

Stay bold. Keep scouting the frontier.