Imagine running a subrogation department for an insurer that captures the full details of the cause of loss for its Property claims, including the origin of the failure, the loss mechanism, and the component or sub-component of loss.
The individual judgment of front-line adjusters would no longer be as crucial in identifying recovery opportunities. As a subrogation manager, you could design and calibrate a rules-based system that flags claims or automatically presents context-specific playbooks to adjusters and your recovery team. The outsized return on investment (ROI) you would achieve by implementing such a system would quickly make it one of your biggest priorities.
Now imagine an underwriting department making use of this same granular data. It would, for instance, be possible to identify specific components (e.g., PipeCo’s pipes) responsible for an outsized frequency of water claims. This data classification then allows for the identification of addressable risks and the rollout of impactful loss-prevention solutions. The insurer becomes a true prevention partner to its policyholders - the ultimate industry win-win.
The above scenario may sound like science fiction, but most insurers are only one step away from this reality. Front-line adjusters tend to know their claims in surprising levels of detail as they handle them. They also tend to record data accurately. The current barrier: insurers are not asking their adjusters to record the cause of loss (‘CoL’) data in a structured way that is useful beyond coverage determination.
One of the most common failings is to ask adjusters to select non-mutually exclusive CoL categories (e.g., “sprinkler”, “freezing”, and “piping”), which prevents the granular grouping of similar claims. Another issue is CoL categories that are insufficiently specific (e.g. “water escape, piping”), and are therefore of little value.
Recording higher-quality CoL data can be done accurately and without placing an additional burden on the adjusters – if anything, when done well, it can even lighten their cognitive load. The key is to design the CoL data structure by considering its end use and the adjusters who will be entering the data. Getting it right can have immense payoffs.
Based on our work with several national carriers, subrogation departments are the ideal project lead for improving how CoL data is captured systematically.
Subrogation professionals, by design, need to go deep and examine all aspects of a loss with meticulous detail to determine the root cause, including all contributing factors. Allowing them to track this work systemically between claims would dramatically improve their efficiency. While precise and meaningful CoL claim data can impact many stakeholders within an insurer, it is in subrogation that its use case and ROI are the most apparent.
The ultimate value in designing a claim data structure that meets subrogation’s high standards will reveal itself in underwriting since the data generated will identify impactful loss factors and prevention opportunities.
This is why carriers looking to improve their loss data collection should actively involve their subrogation departments. Today’s subrogation leaders may hold the key to the future of loss prevention in insurance.
