Angus Black, BarnOwl Data Solutions
In today’s hyper-connected, always-on world, everything revolves around data. Whether in business operations, customer experience, or innovation, data has become the fuel that drives value creation.
In the insurance industry specifically, data is no longer just a historical resource, it’s a real-time asset central to risk assessment, pricing accuracy, claims management, and ultimately, business sustainability.
Having worked closely with numerous insurers and risk professionals, I’ve witnessed how the role of data in risk management has undergone a fundamental transformation over the last decade. We’ve moved from reactive data analysis, using data to figure out why a risk event occurred after the fact, to a proactive model. Now, we can identify patterns and respond to risks in real time, even pre-emptively.
This shift is being driven by both technological advances and increasing expectations from clients, regulators, and reinsurers. Insurers are demanding data that is accessible faster, more integrated across functions, and immediately actionable. Gone are the days when data could be warehoused and assessed long after a policy was underwritten or a claim processed. Today, data must be on tap, always available and always trustworthy.
The building blocks of risk-intelligent data – To enable effective risk management, several categories of data are critical:
- Exposure and underwriting data
At the foundation lies a clear understanding of what’s on cover. What risks has the insurer accepted, to what value, and within what conditions or limits? This directly influences reinsurance decisions and pricing, ensuring that exposures align with the insurer’s appetite and capacity. If this data is incomplete or outdated, it can result in underpricing, overexposure, or missed reinsurance triggers.
- Claims data
Real-time claims data enables efficient resource allocation, especially during catastrophic events. It helps insurers scale teams, streamline processes, and apply risk controls without delay. Fast, accurate claims reporting also supports better customer service and fraud detection.
- Third-party and geospatial data
External data sources, such as weather feeds, crime statistics, and geolocation intelligence, help verify and validate underwriting and claims data. These sources enhance the accuracy of address-based risks, validate bank account or contact details, and support fraud mitigation.
- Predictive and behavioural data
Data analytics now enables us to go a step further — predicting policy lapses, fraudulent claims, or high-risk behaviour before they manifest as loss events. This opens the door to early intervention strategies and more personalised risk solutions.
The hurdles to effective data utilisation – Despite its obvious value, harnessing data effectively remains a challenge for many insurers. Some of the most common issues include:
- Data silos: Different departments — underwriting, claims, finance — often use different systems that don’t speak to one another. Without a unified view of data across the business, insurers struggle to get an accurate risk picture.
- Legacy systems: Older technology systems can make data extraction and integration painfully slow or impossible. This hampers the ability to derive real-time insights or implement modern analytics tools.
- Data quality and accuracy: Incomplete or outdated data skews analysis and decision-making. Regular data cleansing and validation are essential but often overlooked.
- Skills shortage: There is a global shortfall of skilled data professionals who can design, implement, and interpret data analytics. This is further complicated by the rising need for people who understand both data science and insurance.
Predictive analytics and AI – Advanced data analytics and artificial intelligence are transforming how insurers assess and manage risk. In pricing, we’ve moved beyond static rate sheets. Predictive models consider a wide array of dynamic risk factors, market conditions, and client behaviours to generate more accurate and responsive premiums.
In fraud detection, analytics tools flag irregularities in real time, such as an unusual number of transactions with a particular service provider or multiple clients using the same bank account. These tools are essential to keeping ahead of increasingly sophisticated fraud schemes.
AI is also being used for early warning systems in claims management. Predictive models determine whether a claim should be fast-tracked, escalated, or further investigated, saving time and money while improving service levels. Additionally, insurers are beginning to use behavioural data to predict payment issues or adverse claims trends and proactively engage clients before problems arise.
Unifying data – If there is one critical improvement I would recommend for any insurer looking to enhance data-driven risk management, it would be the creation of a unified data platform. This would integrate data from various systems and sources into a single, accessible location.
Such a platform enables holistic analytics, supports regulatory compliance, and helps break down data silos. More importantly, it provides the foundation for agile decision-making, empowering insurers to respond to emerging risks quickly and accurately.
Are insurers taking this seriously? – In my view, yes, but more can be done. The competitive landscape is forcing insurers to modernise. Legacy systems and disjointed processes are expensive, slow, and put insurers at a disadvantage. Those who take data seriously are already seeing the rewards in operational efficiency, pricing agility, product innovation, and risk reduction.
As we look to the future, the role of data will only grow. From embedded insurance to personalised risk solutions and AI-driven underwriting, the winners in the industry will be those who master their data ecosystems and leverage them for smarter, faster, and more responsive insurance.