- The use of modelling to estimate reinstatement costs for insurance offers insurers, brokers and policyholders an opportunity to review large portfolios consistently, cheaply and quickly.
- Modelling can be particularly useful in identifying outliers and unique locations across large numbers of locations that could need more detailed analysis.
- However, the use of online tools or desktop models to estimate reinstatement costs for individual locations is increasingly common. What are the pitfalls of this approach?
- While increased computing power and access to large volumes of location data is adding increased sophistication to analytical tools, the accuracy or applicability of any output from these models relies on the appropriate underlying assumptions made.
- In our experience, the assumptions in many models are rarely fully consistent with the insurance policy terms, the specifics of an individual location or the circumstances of the policyholder.
- If you being presented with the reinstatement cost for a location based on modelling or data analysis, it is vital to drill down into the detail and read the small print!
Insurers want to know that their exposure (and premiums) are built on researched and supportable values at risk. At the same time, asset owners want to have the certainty that they are properly managing risk and are fully covered in the event of a loss.
While there is a role for modelling in identifying outliers and highlighting potential insurance gaps, this doesn’t always equate to the correct expertise to arrive at appropriate declared values for a specific location or facility.
If the assumptions in models do not match to what is expected by the various stakeholders or what is stated within the insurance policy, the insured can end up exposed to underinsurance, or they could pay excess premiums.
This issue is of particular concern when these models rely on the application or adjustment to third party data sources, which in themselves incorporate a number of key assumptions.
Rather than just digitise a traditional process, some firms, including Charterfields, use big data and analytics to better identify historic cost anomalies, cost trends, breakdown of values and factors that materially change replacement costs for facilities.
When choosing how to arrive at declared values, best practice is to review any models for their ability to apply the correct data and approach to the specific assets under consideration.
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