How data science and machine learning are transforming the world of insurance and, more recently, the claims operation.
One way to think about the application of data science and machine learning is that it's a tool to aid the conversion of information (data) into action. In this context, machine learning is applied to enable better and more efficient decisions, as well as identifying previously hidden risks and opportunities. Essentially, data science helps an insurer to perform significantly better, whatever their goals.
The application of advanced analytics is already well ingrained in the world of insurance pricing and underwriting. However, it is only more recently that it has begun to exert more influence in claims operations.
In the overall insurance value chain, substantial resources and effort have been applied to better understand a customer's risk and purchasing behaviours to help charge the most appropriate price. Fresh benefits still to be mined in the pricing and underwriting space are relatively scarce. In contrast, huge untapped value is waiting to be realised by insurers reducing their claims spend or better understanding and optimising their claims processes.
Low hanging fruit
Although machine learning is increasingly recognised as a tool to reduce claims costs and deliver significant value to an insurer, this remains an area many have yet to realise value. This means there is plenty of low hanging fruit to be picked in the claims space, such as the benefits to be realised from providing a better, more tailored, faster service to the customer. These benefits can, for example, be seen by the speed at which claims are settled and how an insurer's Net Promoter Score (NPS), the global benchmark for client satisfaction, can be improved.
Claims processing already uses a lot of external data, including integration into third party sources such as operators in the automotive sales market for vehicle values, demographics and sociodemographic information, and various other vehicle information to inform repair costs. Machine learning makes it possible to link all these separate threads together and help insurance companies more accurately predict future outcomes and identify earlier changing experience.
