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 behaviors 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 realized by insurers reducing their claims spending or better understanding and optimizing their claims processes.