AI helps identify how subrogation and recovery opportunities are missed

Background

When an insurer or organisation suffers a loss or pays a claim against it, or a claim it is liable for, it can either bear that loss itself or consider if a recovery is possible.  For example, if a pub chain suffers an escape of water at one of its properties caused by the negligent work of a plumber, it will pay the repair costs (or if insured its insurer might) and then that cost either comes off its profit for the relevant financial period, or it might decide to pursue a recovery of those losses against the plumber (or more likely the plumber’s insurer).

Subrogation traditionally

Whether to pursue a claim traditionally relied upon the claim handler manually considering, perhaps with the aid of internal policies and processes, whether to pursue a recovery.  This could be frustrated if the handler has more pressing matters or deadlines to attend to, which can impact both the decision whether to pursue a recovery and the resource thrown at making a recovery.

How is AI shining a light?

Emerging AI firms operating in this space suggest:

• AI can be used to identify recovery prospects.  Identifying recovery prospects often takes an enquiring mind and creative analysis of a set of circumstances.  A route to recovery is not always obvious.  For example, a leak of water from a pipe might look like just “one of those things” until one asks when the pipe was installed, and if the answer is yesterday and if an examination of the parts then reveals incorrect installation.  AI might know to obtain and analyse a photograph of the works, to identify very early on whether there is merit in a recovery claim;
• This can be used to increase recovery rates, even doubling them;
• Moreover, productivity and efficiency gains from AI can get around the problem of recovery claims being ‘left’ on the shelf for months at a time with no or limited activity, to shorten the time between identifying recoveries and securing a cash recovery.

Potential practical impact

Imagine for the sake of argument one has say 250 claims a year, with an average value of say £50,000.  If 50% of those claims are missed, due to the focus being on repair or handling the claim itself, or other pressures of work, that could be a potential missed opportunity of £6,250,000.

If average lifecycles are say 18 months to make a recovery, that could be over £12 million delayed until the next financial year rather than being recovered in this financial year.  That can be a further disincentive to pursue recoveries and / or to do so at speed.

These missed and delayed opportunities are bad enough by themselves, but the compounding missed opportunity is the inability to re-invest that cash in the business or organisation.

The future

AI is likely to increasingly assist in identifying missed opportunities, being decisive about prospects, and aid productivity in pursuing claims.

To discuss any aspect of recoveries, contact Chris Heitzman, a consultant in Burges Salmon’s Property and Asset Damage Claims Team.