Floods can strike quickly, leaving a path of destruction in their wake. Insurance companies, tasked with assessing risk and handling claims, are often left scrambling to get answers.
· How many properties are affected?
· What’s their condition?
· How much damage has been done?
These are critical questions that need answers - fast.
This is where 3D building data becomes invaluable. With detailed information about the age of buildings, their materials, height, location, and more, insurance companies can assess risks, streamline claims, and help clients recover faster.
In this article, we'll explore how 3D building data can support insurers before, during, and after a flood like the devastating November 2024 floods in Spain.
1. Pre-Flood: Understanding What’s at Risk
Floods are unpredictable, but that doesn’t mean you can’t be prepared. With 3D building data, insurers can gain a clear picture of their entire portfolio before disaster strikes.
How Many Buildings Are Vulnerable?
The first step in managing flood risk is knowing how many of the buildings you insure are vulnerable. Geospatial data lets you map out your entire portfolio and identify properties in flood-prone areas. Combine this with elevation data, and you’ll know exactly which buildings are at higher risk of flooding—and more importantly, where they are.
For example, buildings in low-lying areas or near rivers could be facing greater exposure. With this knowledge, you can adjust premiums or recommend flood-resistant measures to your customers.
What’s the Value of These Properties?
Not all buildings are equal when it comes to risk. By understanding the size, materials, and age of each building, insurers can estimate their replacement cost more accurately. For instance, a large, older building made from wood will be at much greater risk—and likely cost more in damages—than a smaller, modern structure made of concrete.
This data gives you the power to fine-tune your premiums, ensuring that you’re offering the right coverage at the right price.
What About Zoning?
Zoning data provides another critical layer of insight. Some buildings might be located in high-risk zones or areas with strict building regulations, which can impact how they’re insured. Buildings that comply with modern flood-resistant codes could qualify for lower premiums, while those in zones with more frequent flooding might need special attention in terms of coverage.
2. During the Flood: Real-Time Risk Management
When floods hit, insurers need to act quickly. 3D building data doesn’t just help you prepare - it helps you manage the crisis in real time.
Which Properties Are Affected Right Now?
Flooding is dynamic, and so is the data you need to manage it. By integrating real-time flood maps with geolocation data, insurers can track which buildings in their portfolio are currently affected by floodwaters. This allows you to prioritize customer outreach and prepare for claims before they start piling up.
For example, knowing which buildings are in the path of rising waters lets you contact those policyholders early, offering them advice or emergency support. It also helps you brace for an influx of claims, so you’re ready to respond when the time comes.
How Much Damage Can I Expect?
Not all buildings will suffer the same level of damage during a flood. The building’s materials, age, and size play a huge role in determining how much damage will occur. A wooden structure with a basement in a floodplain? That’s going to need serious repairs. A single-story concrete building? It’s likely to come out with far fewer issues.
By combining real-time flood data with your building information, you can generate early estimates of potential damage. This helps you gauge the financial exposure in real time, so you can manage your reserves and prepare for payouts efficiently.
3. Post-Flood: Streamlining Claims and Recovery
Once the floodwaters recede, the flood of claims begins. Processing these quickly and accurately is critical, and 3D building data helps you do just that.
How Much Damage Has Each Property Sustained?
The faster you can assess damage, the faster you can process claims. With pre-flood 3D building models and post-flood aerial or drone imagery, you can quickly compare a building’s condition before and after the flood, giving you a clear picture of the damage.
This comparison process speeds up claims processing by reducing the need for on-site inspections. Instead of sending out adjusters to inspect each property, you can validate claims remotely—saving time, money, and, most importantly, helping your customers rebuild faster.
4. Long-Term: Managing Future Risk
Floods aren’t a one-time event. With climate change increasing the likelihood and severity of natural disasters, insurers need to think long-term. One of the most powerful tools in this effort is machine learning (ML).
How can Machine Learning Improve Flood Risk Management?
Machine learning models thrive on accurate and granular data. They analyze patterns in historical data to predict future risks, enabling insurers to make better decisions about premiums, coverage, and portfolio management. However, the output of these models is only as good as the data they’re built on.
How Our Data Enhances Machine Learning Models
3D building data goes far beyond simple geolocation. Attributes like building materials, age, size, elevation, zoning, and use provide the rich detail that ML models need to improve both accuracy and precision in risk predictions. Here’s how these attributes can make a difference:
- Building Materials: Machine learning models can use data on materials (e.g., wood, concrete, steel) to predict how different structures will perform in flood conditions.
- Building Age: Older buildings often lack modern flood-resistant features or may have deteriorated structurally over time.
- Elevation Data: Flood risk isn’t just about location—it’s about height. By incorporating elevation relative to floodplains, ML models can more accurately predict which buildings will be affected by rising waters.
- Building Size and Layout: Larger buildings or those with basements tend to sustain more extensive—and expensive—damage during floods.
- Zoning and Building Use: Zoning data and building use (e.g., residential, commercial, industrial) provide context that ML models can use to predict financial impact. For instance, a warehouse full of expensive inventory is a higher financial risk than a small residential home.
And the Outcomes for Insurers?
Here’s what you gain:
- Improved Risk Predictions: ML models can predict flood risks with greater accuracy. Insurers can identify which properties are most likely to flood, how much damage they’ll sustain, and what the financial cost will be.
- Dynamic Premium Adjustments: ML models can analyze patterns in historical floods and simulate future scenarios, allowing insurers to dynamically adjust premiums based on real-time risk factors. This ensures that premiums are fair, competitive, and reflective of the actual risk.
- Portfolio Optimization: With ML models powered by detailed building data, insurers can optimize their portfolio by identifying high-risk properties and taking proactive steps—whether that’s recommending flood-resistant upgrades, increasing premiums, or adjusting coverage terms.
- Scenario Planning: ML models can simulate hypothetical flood events, helping insurers understand the potential impact of future disasters. For example, how would a 1-in-100-year flood affect your portfolio? With robust data, the answers become clearer.
Predicting Future Losses with Historical Flood Data
One of the most exciting applications of machine learning is its ability to learn from the past to predict the future. By feeding ML models with historical flood data, insurers can create predictive models that are constantly improving.
For instance:
- A building that consistently sustains damage in moderate floods may be flagged as a high-risk property.
- Conversely, properties that show resilience despite being in flood-prone areas can be identified for premium reductions or incentivized upgrades.
This iterative learning process allows insurers to refine their risk models over time, ensuring they stay ahead of emerging risks.
Encouraging Resilient Construction
Machine learning insights don’t just help insurers manage risk—they can directly benefit customers and communities by encouraging smarter, more resilient building practices. For example:
- ML models can identify building types and materials that perform best during floods, helping insurers recommend flood-resistant construction techniques to their policyholders.
- Insights from these models can also be shared with urban planners to advocate for smarter zoning regulations and flood mitigation strategies, reducing the overall impact of future disasters.
The Bigger Picture: Data-Driven Resilience
Floods aren’t going away, but insurance companies can adapt—starting with better data. By combining your 3D building data with machine learning, insurers gain a powerful competitive advantage: the ability to predict future risks with confidence, price policies more effectively, and reduce losses over time.
This isn’t just about protecting the bottom line. It’s about building a future where communities are better prepared for floods, buildings are more resilient, and insurers can act as true partners in recovery and prevention.
