Risk managers are not short of data.  Where true value from data comes in though, is in having decision-ready insight.  

Claims information is usually dispersed across insurers, TPAs, brokers and various internal systems, each providing a partial, disjointed view of performance. For large organisations, the priority is to create  a trusted, single version of the truth that helps with analysing claim costs, challenging assumptions and supporting better commercial, strategic and governance decisions. 

At HF, we bring value to analytics by bringing data to life. Claims insight from that data can help risk managers reduce costs, improve renewals, challenge insurers, justify risk-financing decisions and identify emerging risks before they develop into major losses, therefore demonstrating clear measurable value. 

Greater visibility of claims performance 

Real value comes from turning information into answers. Risk managers need to know what’s driving losses, which claims are deteriorating, where leakage is occurring, whether reserves are developing appropriately and which business units, locations or suppliers are creating or carrying disproportionate risk. 

Claims analytics provides visibility across the portfolio. It allows risk teams to move beyond isolated loss runs and create a joined-up view of claim performance, reserving movement, leakage, deterioration and operational roadblocks. That visibility is central to reducing Total Cost of Risk (TCOR) because it highlights the claims, locations and behaviours that have the most material financial impact. 

Identifying emerging risks earlier facilitates intervention  

Structured correctly, claims data has the potential to identify emerging risk trends months or even years before they appear in traditional risk registers. Increasing injury severity, escalating litigation rates, supply chain failures, property claims inflation, ESG-related losses, cyber incidents and changes in fleet or mobility risk can all be visible in claims activity before they emerge as major loss trends. By detecting these movements earlier in the claims lifecycle, risk managers can move from retrospective analysis to proactive intervention. 

Using claims data to strengthen renewal discussions 

Data-driven insight strengthens insurer and broker engagement. At renewal, risk managers need to understand why premiums have increased, whether loss trends are genuine or reserve driven, which claims characteristics are driving severity, where risk has improved and which controls have been most effective. That shifts the conversation from accepting market commentary to testing and challenging it with data-driven evidence. 

The difference is significant. Instead of being told, “losses are deteriorating”, a risk manager may be able to demonstrate that 70% of incurred cost inflation comes from a small group of legacy claims, a particular injury type or a limited number of unresolved reserve movements. That creates a much stronger negotiating position and provides a clearer and more solid base for strategic pricing, programme design and claims approach. 

Optimising risk-financing strategy 

In-depth data analytics supports better risk-financing and renewal decisions. For larger corporates, decisions on deductibles, self-insured retentions, captives, aggregate programmes and stop-loss arrangements increasingly require evidence that they’re based on current risk behaviour and not historical precedent.  

Scenario mapping adds further layers of value by helping organisations strength-test how regulatory change, claims inflation and an ever-changing economic landscape is likely to impact future cost, exposure and performance. Risk managers are able to explain what risk-financing structure is recommended and why it is resilient under a range of plausible and calculable future conditions. 

Measuring insurer and claims service performance 

An area of growing interest is insurer and TPA governance. Risk managers increasingly want to understand time to first contact, lifecycle to settlement, litigation rates, reserve adequacy, rehabilitation performance, escalation points and outcome variation between handlers. These measures create a more objective basis for service reviews, performance improvement/challenge and improvement planning. 

Moving from reporting to prediction 

The most forward-looking organisations are moving from reporting to prediction. Rather than asking only what happened last quarter, they’re using data to ask which claims are most likely to become high-cost losses, which incidents have the highest statistical risk of litigation and where next year’s losses may emerge. This aligns strongly with current interest in AI, predictive analytics and proactive risk management, all of which are central pillars of the HF service offering and strategy. 

The role of risk managers is evolving from reporting on losses to using data strategically to influence organisational resilience, insurance purchasing and future risk outcomes. In a market shaped by inflation, regulatory pressure and emerging risks, the organisations that gain decision-quality insight from claims data will be better placed to reduce TCOR, challenge insurer assumptions, justify risk-financing decisions and demonstrate the true and measurable value of risk management. 

 

Simon Forster is Partner and Head of Predictive Analytics & Data Insights, specialising in applying advanced analytics, predictive modelling and AI to help insurers and corporate clients improve claims performance, risk management and strategic decision-making.

To find out more, please get in touch.