Breaking Down Silos: Data’s Role in Risk Management

In this episode of COVER Magazine’s podcast, Tony sits down with Angus Black, Director at BarnOwl Data Solutions, to unpack the central role of data in modern insurance. As Tony notes, “everything is about data”, and Angus delves deep into how insurers are leveraging it to manage and mitigate risk more effectively.

Angus explains how the use of data has shifted from reactive, analysing incidents after they occur, to proactive, with real-time insights enabling insurers to act immediately. He identifies core data types essential for underwriting and risk management: exposure data, claims data, and increasingly, third-party verification and geospatial data to validate and enrich existing information.

The conversation also covers pressing challenges: entrenched data silos, outdated legacy systems, and the global shortage of skilled data professionals. Angus emphasizes that poor data quality and disconnected systems hinder effective decision-making. His solution? Investing in a unified data platform to improve analytics, ensure compliance, and enable agile risk management.

Highlighting real-world applications, Angus shares how predictive analytics are already reshaping pricing, fraud detection, and early warning systems, from identifying suspicious claims to forecasting payment issues before they arise.

This engaging discussion underscores how insurers who prioritize data innovation will not only manage risk more efficiently but also unlock new opportunities in product design, service delivery, and competitive positioning.

Key Points

• Proactive Data Use: The shift from using data after an event to using it in real-time to prevent or mitigate issues.

• Types of Crucial Data:

• Underwriting exposure data

• Claims data

• Geospatial and third-party validation data

• Challenges:

• Data silos within and across systems

• Legacy systems that resist integration

• Inaccurate, incomplete, or outdated data

• Shortage of skilled data professionals

• Predictive Analytics Applications:

• Dynamic, model-based pricing

• Fraud detection through anomaly spotting

• Early warnings for potential payment or claims issues

• Strategic Advice:

• Invest in a unified data platform to integrate data sources, ensure data quality, and enhance reporting.

• Competitive pressure is pushing insurers to improve their data strategies for better pricing and efficiency.