Using regression analysis to uncover claims cost escalation

Claims inflation rarely has a single cause. A rise in average settlement value may reflect building materials, wage pressure, supply delays, legal expenses, weather severity, policy design, or a change in the mix of claims. Regression analysis gives insurers a disciplined way to separate these influences and identify which factors are contributing most to rising costs.

For Australian insurers, this analysis is increasingly important. A property claim in Brisbane may be affected by flood exposure and contractor shortages, while a similar claim in Melbourne may be shaped by construction inflation, colder-weather repairs, or local labour availability. The same portfolio can therefore contain several different cost stories.

Used well, a regression model does more than explain historical results. It can support pricing, reserving, claims triage, reinsurance decisions, supplier negotiations, and operational planning. It can also help finance and claims leaders communicate clearly about whether cost escalation is temporary, structural, or associated with changes in portfolio risk.

Define the claims cost problem precisely

The first step is to decide what “claims cost” means for the analysis. An insurer may study incurred loss, paid loss, ultimate loss, average cost per claim, cost per exposure, or the time taken for a claim to reach settlement. Each measure answers a different business question.

Average cost per closed claim is useful when examining settlement severity, but it may be distorted if simple claims are being settled quickly while complex losses remain open. Incurred development can provide a broader view, although it depends on reserving practice. A practical analysis often separates frequency, severity, and development behaviour rather than combining them immediately into one ratio.

The time period also matters. Monthly data can reveal sudden changes in repair prices or catastrophe activity, while quarterly data may reduce random volatility. Analysts should account for claim notification dates, accident dates, settlement dates, inflation adjustments, and case reserve movements. Without consistent definitions, a technically impressive model can produce misleading results.

Australian portfolios need careful treatment of catastrophe events. Floods in northern New South Wales, bushfires affecting regional communities, and severe storms in Queensland can produce clusters of correlated claims. These events should be identified with catastrophe indicators or separate event categories rather than treated as ordinary observations.

Build a data set that reflects claim reality

A useful regression data set connects claim outcomes with characteristics known at policy, exposure, claim, and external levels. Potential variables include sum insured, building age, location, construction type, excess, occupation, cause of loss, claim channel, repair pathway, lawyer involvement, supplier type, days to assessment, and days to settlement.

External variables can explain cost movement that is invisible in internal claims records. Building material indices, wage measures, fuel prices, weather severity, consumer price inflation, exchange rates, and regional labour availability may all influence settlement amounts. For motor claims, parts shortages, vehicle age, repair complexity, and electric vehicle penetration can be relevant.

Data quality often determines the value of the model. Duplicate claims, inconsistent postcode formats, missing cause-of-loss codes, and changes in claims platforms can create false patterns. A coding change introduced during a system migration may look like a genuine shift in litigation or severity. Analysts should record data lineage and flag periods where definitions changed.

Australia’s geographic scale creates particular challenges. A supplier network covering Sydney, Perth, and Darwin will face different transport costs, contractor capacity, and repair times. Regional and remote claims may have higher average expenses because equipment and specialists travel further. Location should therefore be represented at a useful level, such as state, region, remoteness category, or climate zone, rather than relying only on a national average.

Select a model that matches the outcome

Ordinary least squares regression is easy to explain and can be a suitable starting point for adjusted average costs. It estimates how much the expected claim amount changes when a driver changes, while holding other included variables constant. However, claims data is often skewed, with many small losses and a small number of very large ones.

A log-transformed severity model, Gamma regression, or a generalised linear model may better reflect that distribution. Frequency can be modelled separately using Poisson or negative binomial methods, while a two-part model can address the distinction between having a claim and the amount paid once a claim occurs. For open claims, survival analysis can help explain settlement delay and the resulting expense.

Time series features are important when studying escalation. A model can include month indicators, trend terms, lagged repair-cost indices, and catastrophe flags. Lagged variables are particularly useful because supplier prices may change before those changes appear in final settlements. A simple model that uses current inflation alone may miss this delay.

Interactions can reveal operational realities. For example, the effect of building age may be stronger after a severe storm, and the effect of regional location may be greater for complex commercial property claims. These terms should be added selectively and interpreted carefully. A model with too many interactions may fit historical data closely but perform poorly when conditions change.

Test whether the apparent drivers are reliable

A coefficient is not automatically a causal explanation. If claims handled by external suppliers cost more, that may reflect the severity of claims assigned to those suppliers rather than poor supplier performance. Similarly, claims involving legal representation may already be more complex before a solicitor becomes involved.

Analysts should examine confidence intervals, statistical significance, residual patterns, and out-of-sample performance. Cross-validation or a holdout period can show whether the model predicts future claims rather than merely describing the past. Comparing predicted and actual costs by state, product, claim type, and severity band can expose hidden weaknesses.

Multicollinearity is another risk. Building age, construction type, insured value, and location may be closely connected. When explanatory variables overlap heavily, individual coefficients can become unstable even when the overall model predicts well. Correlation checks, variable selection, regularisation, and carefully designed categories can make the results easier to interpret.

The most valuable output is often a driver decomposition. This shows how much of the movement in claims cost is associated with exposure mix, economic inflation, catastrophe activity, repair duration, legal involvement, or other factors. It gives executives a more useful narrative than a statement that “costs increased by 8 per cent.”

Convert results into claims and finance decisions

Regression findings should lead to specific operational responses. If settlement delays are associated with higher severity, claims leaders may prioritise early assessment, authority limits, supplier capacity, or digital documentation. If certain materials or repair types account for a disproportionate increase, procurement teams can review contracts, preferred networks, and alternative repair methods.

Pricing and underwriting teams can use adjusted cost indications to distinguish portfolio deterioration from changes in risk selection. Reserving actuaries can use the analysis as one input into assumptions about development and future inflation. Finance teams can monitor whether actual results are tracking the drivers used in forecasts rather than relying on a single headline loss ratio.

Supplier analysis requires fairness and context. A contractor operating in regional Western Australia may show higher average costs because of travel and limited local availability. A repairer handling severe hail damage may appear expensive because of claim complexity. Regression adjustment can help compare like with like, while also identifying genuine differences in cycle time, rework, or invoice leakage.

The same discipline applies to event and workplace risk. Organisations running conferences or customer-facing programmes need controls around food handling, venue access, cleaning, and incident reporting. Clear event hygiene practices can reduce operational uncertainty and provide better records if an incident occurs. These details may sit outside the claims model, but they support the broader risk management environment in which insurance decisions are made.

Embed the analysis in governance and professional practice

A regression model should have an owner, a review cycle, and clear documentation. Governance should cover data sources, variable definitions, model purpose, limitations, approval thresholds, and the circumstances that trigger recalibration. A model built after a major flood may need adjustment when catastrophe conditions normalise.

Model monitoring can include prediction error, drift in input variables, changes in claim mix, and stability of key coefficients. If average repair duration rises sharply in Victoria or a new claims platform changes coding behaviour, the model may require investigation before its outputs are used for pricing or reserves. APRA-focused governance expectations also make transparency and control important for insurers operating in Australia.

Cross-functional review improves interpretation. Claims specialists understand workflow and settlement decisions; actuaries understand uncertainty and development; finance teams understand reporting impacts; operations leaders understand supplier constraints; and technology teams understand data capture. Bringing these perspectives together reduces the risk that a statistical association will be mistaken for a practical cause.

Industry events can support that collaboration. The IASA vendor connect resource can help teams investigate analytics platforms, claims technology, data services, and implementation partners. Conversations with vendors should focus on auditability, integration, explainability, security, and the ability to monitor model performance rather than on dashboards alone.

Professional networks are equally valuable when the issue crosses departmental boundaries. Informal discussions about reserving assumptions, fraud indicators, automation, and catastrophe response can reveal how other Australian insurers are addressing similar cost pressures. The IASA networking programme provides a setting for those conversations alongside education on finance, technology, risk, and customer administration.

A strong regression analysis turns a broad concern about claims inflation into a prioritised set of actions. It can show whether rising costs are driven by exposure, economics, process, suppliers, or extreme events, while making uncertainty visible. The result is better forecasting and more targeted intervention.

Bring claims, actuarial, finance, operations, and technology leaders together to review the variables behind your portfolio’s cost movement. Use the evidence to test assumptions, strengthen supplier and reserving decisions, and build a repeatable monitoring process that keeps pace with Australia’s changing insurance market.