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Achieving Pricing Maturity - A Digital Transformation Roadmap

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Summary

Pricing optimization is the most significant driver of sales and underwriting profit, and an imperative in these unprecedented times as Insurers struggle to provide value. Traditionally, pricing has been a ‘walled tower’ exercise undertaken by underwriters and product manufacturers based on periodic input from the market. Leaders in pricing however, are doing much more to change this traditional approach as a recent study from McKinsey1 put forth. This whitepaper operationalizes these findings into a roadmap for pricing innovation for organizations.

How leaders in pricing operate

From creating events that help understand purchase causality to promoting a culture of pricing awareness, industry leaders in pricing are changing the rules of the game. This approach in turn is reaping rich dividends. In the era of low interest rates and commoditization of innovation, sharp pricing driving underwriting profit is the best driver of operating profit and return on investment. McKinsey outlines five levels of pricing maturity in their study, and here, we attempt to operationalize a pathway to achieving them.

The foundation – consistent use of ‘unconstrained’ generalized linear models in underwriting

Underwriting used to be known as both an art and science. However, with the growth of predictive analytics and machine learning, the verdict is clear – science is where the future lies. Moving beyond simple linear rate cards, a solid foundation to move towards pricing maturity lies in using generalized linear models with unconstrained variables in the pricing equation. This is a starting point in pricing maturity and more than half of the P&C companies in the US and Europe have already established this as a foundation (and therefore are in good stead). For those that have not, the adoption of a GLM (Generalized Linear Model) software in underwriting is table stakes.

Institutionalizing AI in pricing

Introducing Artificial Intelligence (AI) into the pricing process allows companies to leapfrog into sophisticated pricing. Typically, historical data from policy admin and sales CRM system is used to train the model and drive pricing changes in annual filings. An acceptable step taken in today’s environment is to invest for future scale and build AI in the cloud from the get-go (as opposed to an offline solution). This can lay the foundation for real-time inputs and dynamic pricing in the near future.

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