Analytics, foundation subsidence and the $300m claims ’emotional rollercoaster’

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‘Foundation subsidence’ is an insured peril which is particular to the UK insurance property market. Subsidence occurs around the world, but only the UK market gives cover, and has cost the UK insurance industry in excess of £3.0 Billion ($4.8 Bn) since it started in the mid-seventies.

It relates to damage to the home, when the foundations ‘sink’ due to the impact of foundation movement, often from the interaction of trees on shrinkable clay soil beneath the foundations, or leakage from defective drains. At its simplest, buildings become cracked and often unsaleable. At worst, the buildings fall down.

Damage prediction is difficult. Take the challenge of the impact of trees alone: the critical factors are height, trunk size, crown size, extent of leafage, distance from the house, impact of other adjacent trees. Then add the different types of building construction, foundation type and depth, and location.

There are about 20,000 claims each year with an average settlement figure of £7k, so the market cost is about £150m, ( about $250m ) each year. The algorithms are complex and they stretch predictive and maybe even cognitive analytics to the limit. All very interesting, but it’s probably that the technology and insurance industries aren’t going to prioritise the prediction and solving of subsidence problem as being a global focus area. There isn’t the scale, the problem is too localised to one country, and it falls into the ‘too difficult’ box for the moment.

Possibly a good challenge for a niche UK player?

Interestingly, the subsidence industry is also trying to get its collective head around whether there is a type of telematics solution for the home, which monitors the movement of floors and walls, rainfall, tree growth, ground conditions.

But perhaps it’s not really about the technical side of managing these complex claims, but it’s about managing the customer experience, through what we describe as the ‘emotional rollercoaster’.  The ‘emotional rollercoaster’ is a good description of the ups and downs of how a customer feels during the claims duration, usually 18-24 months.

If we can’t predict physical damage, can analytics can help manage the individual experience of each of the 20,000 annual claimants, through better understanding of their personal behaviour and requirements during the claims process?

Does the way someone buys insurance might give a clue as to how they want to be treated at the point of claim? Does someone who buys insurance through a agent, for example, want to have a more personal treatment during the claims process? And does an on-line purchaser also want to check the progress of their claim online, maybe using their mobile device?

Customer analytics has historically being aimed at acquisition, growth and cross selling. The most savvy insurers are seeing that profitable growth is also linked to the service experience, even including when their customer makes a claim. The relatively niche challenge of subsidence might offer new insights in creating a differentiated ‘analytically driven’ customer experience, in the area of low volume, complex claims handling?

After all, we describe the claims process as the ‘moment of truth’ in insurance. One size doesn’t fit all, and don’t we all like to be treated as individuals?

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