Insurance has always been about data—your driving habits, your past claims, the weather in your area. But what if your refrigerator contents, your social media activity, or even your dog’s vet records determined your insurance premium?

It’s not a futuristic fantasy anymore. Thanks to the explosive growth of Big Data and InsurTech, insurance providers are stepping into a world where hyper-personalisation isn’t just possible—it’s already happening. According to Darran Simons from FICO, the real challenge isn't collecting the data, but knowing how to analyse it effectively. And right now, that’s where the insurance industry is falling behind.

Skills Shortage: The Hidden Bottleneck

In April 2023, Forrester’s research painted a concerning picture. While 81% of insurers said they could access third-party data, only 12% had strong expertise in analysing it. The result? A massive opportunity gap between what’s possible and what’s actually being done.

This gap means that while the technology to revolutionize underwriting is readily available, many insurers are stuck using old models simply because they lack the right people and platforms to process modern data.

Alternative Data: A New Goldmine

‘Alternative data’ is the term for non-traditional information sources that aren't usually part of insurance forms or claims. This can include:

• Psychographic profiling: Deep dives into consumers' values and attitudes to predict behavior.
  
  
• Smartphone usage patterns: With user permission, insurers can assess habits like exercise and screen time to evaluate health risks.
  
  
• Drone imagery: Providing safer, quicker, and non-invasive ways to inspect properties after natural disasters.
  
  

The possibilities don’t stop there. Insurers could soon tap into data from smart appliances, pet ownership databases, fitness trackers, safe driving scores, and even educational backgrounds to get a full, 360-degree view of their customers.

By embracing these new data sources, insurance companies could achieve better customer-centricity, faster claims processing, more accurate underwriting—and potentially, higher profits.

Data-Driven Decisions: The Need for Better Platforms

To fully harness this new wealth of information, insurers must not only collect data but operationalise it across their businesses. Machine learning models, decision simulations, and AI-driven insights can create a dynamic underwriting system that evolves with every customer interaction.

However, without robust decision platforms, even the best data is useless. According to Forrester, 82% of firms acknowledge the need for a centralized Digital Decisioning Platform (DDP), but 38% don’t even know where to start building one. Many struggle with siloed, incompatible data, and outdated tools.

A DDP can transform how companies use data by:

• Seamlessly integrating new data sources through APIs.
  
  
• Allowing business users (not just IT) to model decision impacts easily.
  
  
• Speeding up operational rollout of new underwriting strategies.
  
  

When these platforms are in place, companies can simulate outcomes, reduce IT bottlenecks, and empower teams to drive innovation at lightning speed.

Proven Alternative Data Sources Already Making Waves

Several alternative data streams are already showing measurable benefits:

• Credit bureau insurance scores: Strong predictors of claims propensity and payment reliability.
  
  
• Property and vehicle data: Minimizes underwriting guesswork and application questions.
  
  
• Social media and open-source insights: Understand customer sentiment and lifestyle risks.
  
  
• Vehicle telematics: Real-world driving behavior data supporting usage-based insurance.
  
  
• Health records (with consent): A critical tool for health and life insurance risk models.
  
  
• Geospatial data: Pinpoints environmental risks like floods or fires.
  
  
• Employment and income: Stability indicators for underwriting credit or life policies.
  
  
• Claims history: Past patterns predicting future behavior.
  
  
• IoT device outputs: Real-time monitoring to prevent claims.
  
  
• Customer behavior analytics: Predicts loyalty and potential churn.
  
  
• Consortium data: Combats fraud through pooled industry intelligence.
  
  

Each of these data types brings insurers closer to an underwriting world that is dynamic, predictive, and customer-first.

The future of insurance isn’t just about protection—it’s about prediction, personalization, and prevention. With the right skills and platforms, insurers can move beyond reactive models and build offerings that truly meet individual customer needs.

But to reach that future, insurers must first close the skills gap, invest in interoperable systems, and champion a data-driven mindset across their organizations.

Those who embrace the change will not only outpace their competitors—they’ll redefine what insurance means for a new generation of customers.