In the latest in our series of blogs focusing on data transformation in General Insurance we explore the value of data to the pricing and underwriting function within the context of a broader data transformation programme.
At its most simple, insurance can be defined as the pooling of a community’s resources to provide financial protection against potential risks. It therefore follows that an insurer’s ability to better understand and price risk will result in fairer and more reflective premiums for their customers. However, whilst the quantity of data theoretically available to those in the pricing and underwriting functions has increased at a dramatic rate, the ability to translate this into meaningful and quantitative insights (passed onto the customer in the form of premium reductions) has not kept pace. In addition, as well as providing fairer premiums to customers there are large potential gains to be made to the bottom line if the pricing and underwriting teams can capitalise on, and draw insights from, data now available to them. Across many of the large insurers even a one or two percentage improvement in pricing accuracy could translate into many millions through improved loss ratios.
However, despite the obvious benefits to both customer and company, many insurance firms still struggle to extract insight from these new sources of possible information. Symptomatic of years of underinvestment in legacy infrastructure and ways of working, the journey to overcome these hurdles will for most insurers not be an insignificant one. However, if we assume success what sort of impact can we expect to see to the pricing and underwriting function?
- More time spent on high value activities.
Instead of spending time extracting and manipulating data from multiple sources Actuaries and Underwriters will be able to spend more time analysing and drawing insights from the data now available to them. Greater trust in the accuracy of the data will also mean less time spent validating and reconciling.
- Access to data from less traditional sources.
Using information made available through the multitude of new sources now available such as telematics black-boxes, wearables, and smart sensors to draw tangible insights into the way risk exists in our lives and then price accordingly and accurately.
- Models built using structured and unstructured data.
Create pricing models that are able to blend different types of unstructured and structured data sources to create multi-dimensional views of potential risk.
Of course these are just a few examples of how improved access and quality of data will impact the pricing and underwriting teams. What is certainly guaranteed is that this picture will continue to evolve as we come to better understand how data can be used in a pricing context. Moreover, it is also important to remember that these functions do not work in isolation and indeed the ‘insurance lifecycle’ ensures that improved data availability and usage in pricing and underwriting will benefit other departments and teams. For our thoughts on data transformation and the claims department watch out for the next blog in our series.