Data can be powerful.

That makes a huge difference when you're calculating risk at property level. There is a world of difference between "right next to the river and liable to flood", and "right next to the river but is a Victorian terrace, situated 35m above the highest recorded flood level."

Pinpoint property location is the key to most risk assessment for individual properties. The numbers of bathrooms, bedrooms, basements and sub-basements; the extent of boundaries, proximity to trees, and the extent of tree canopies; flood types, subsidence risk related to current and previous year weather conditions… the list goes on. But granular information is only one part of the story.

As more insurers discover the value in granular location data, the true gains are being made in combinations of detailed datasets. The finer points of a risk assessment may only reveal themselves through the comparison of two or more datasets - and that leads to the third challenge. 

Joining the dots

There's no better way to describe it. Making strong links - joining the dots - between entries in vast property, location, spatial and risk datasets is a costly and time-consuming task for any business. There's also an increasing need to add more environmental and geological considerations into the mix than ever before, and subsidence and geoclimatic data, commercial ownership data, title and tenure data, flood risk mapping, and UK crime modelling can all tip the balance in a risk analysis. It's essential to join the dots accurately - and to keep the costs down, at the same time.

At this point, some insurers struggle due to the sheer magnitude of data in play and the number of teams needing to access it, efficiently. It is true, a strong business model will factor those costs in, but the market is tight. Competitive costing is also key, which begs even more questions – where have datasets come from? Are they all sourced centrally? How are they being compared, or linked? And if there's more than one supplier, what are the guarantees around data quality and consistency? 

Anyone who handles data for risk management knows currency may be the biggest challenge in linking datasets with confidence.

The answer to all three challenges

Expense, quality, and joining the dots. Three challenges, all rolled into one. However, there is an effective way of overcoming them. It involves choosing licencing that works the way you do, procuring data that's curated for accuracy and currency - and, more importantly, paying for access to the property attributes that are relevant, not the ones that aren't - and making sure the Unique Property Reference Number (UPRN) is integrated at source, as the key to linking datasets with confidence.

In fact, done properly, the act of overcoming challenges that are holding the quality of your risk analysis back can also pinpoint hidden risks, surface value, and cut costs at the same time. 

Find out how