Model risk management for insurance

As technology advances, the ability to analyse massive amounts of data is driving up the requirement within the insurance industry for well governed models that can be quickly deployed. Insurers recognise the opportunity that large quantities of structured and unstructured data from new sources bring to develop AI and Machine Learning models.

The benefits of this scale of modelling can include improved pricing, offering more tailored and relevant products, better fraud detection and improvements in customer service. At the same time, regulatory requirements are looming.

Model risk is the risk inherent in using models to predict requirements, forecast demand and inform decision making; the possible adverse consequences of decisions based on models that have fundamental errors or the misuse of those models. Any new regulation for banks or insurers is likely to place direct responsibility for group wide model risk management onto a senior director.

This content highlights the implications for insurance providers and why a robust approach to model risk management is required.