One of the key benefits of predictive modelling in group insurance is more accurate forecasting on how to price insurance products. In fact, over 60% of insurers say predictive analytics has helped them improve profitability.
From tweaking factors on rates to entire rate recalculations, predictive models can be used to optimize group pricing. Predictive models use machine learning to analyze rates on won and lost quotes in conjunction with demographic and behavioural profiles of groups to suggest pricing improvements.
While incorporating external data into a predictive model is valuable, there are limitations to this approach: it can be costly and time-consuming.
A carrier can mine its own product experience data to support a predictive model that can surpass traditional actuarial methodologies. A good predictive model can assess multiple data points simultaneously, identifying important trends and correlations.
Additionally, predictive models can provide actuarial and pricing teams tools to identify trends and key factors in both won and lost cases to improve close ratios. Predictive models will also play a more prominent role in renewal underwriting by suggesting adjustments to retain profitable customers.