The Top 6 Myths of Non-life Pricing Insurance

Pricing often occupies a paradoxical position in an insurance company: All CEOs consider this function to be at the very core of their business, growth and profitability; yet few give their technical management the means to achieve their ambitions in this area. Underwriting or distribution often take precedence in the order of priorities of business as usual.

Moreover, the pricing process is very often accused of being slow, disconnected from the threats of the market or the competition, or even opaque and incomprehensible, to the point of becoming almost anxiety-provoking: «in the end, the less we use this service, the better off we are! ». This could have been the subject of an exegesis of a Myth in itself, as pricing is so important for the growth and profitability of a company… and it is unfortunately often the case that a lack of human resources and tools is at the root of the problem that prevents pricing from fulfilling its role correctly.

The experience we have gained over the last 25 years during our numerous missions around the world has enabled us to understand and respond to the many beliefs surrounding pricing. We have selected 6 Myths around pricing, which seem strangely persistent and widespread within all types of non-life insurance companies.

Myth N°1 « Our non-life underwriting process is truly aligned with our strategic objectives »

• Are you so sure that your company’s pricing practices and processes are aligned with your strategy for sustainable and profitable growth?
• Are you sure that you have all the parameters for analysing the cost of your risks?
• Do you have the granularity to make strategic or marketing decisions based on your actual risk appetite policy?

Myth N°2 « Producing a tariff is the only one issue in pricing »

• In your view, does the ‘tariff’ represent the pure premium or the commercial premium?
• What work is required upstream or downstream of the premium calculation and should be integrated into the pricing process?

Myth N°3 « The use of GLM pricing models guarantees me sustainable and profitable growth »

• Are your tariff models already based on generalized linear models (GLMs)?
• When defining commercial premiums, how do you take into account the behaviour of your policyholders, price elasticity, your competitive positioning or the impact of the offer of new entrants, sometimes very disruptive?
• How quickly do you produce a new tariff in the market?

Myth N°4 « The redesign of tariffs is only a case by case issue and investing in a tool is not necessary »

• Do you think you can withstand the competitive pressure of your current competitors and new entrants without a competitive advantage in your core business, i.e. pricing?
• How can you audit your models and studies without the help of a tool?
• How can you be sure that your process is complete if it is not guaranteed by a tool that holistically integrates the process itself?
• How can you gain experience and agility by starting from scratch with each analysis?

Myth N°5 « Pricing is a matter of individual expertise »

• What risks does the company face today if the holder(s) of part of the pricing knowledge leaves, for whatever reason?
• How can you reduce your operational risk tomorrow, by not relying on the presence of one key person for all or part of the success or failure of your profitable growth strategy?
• Conversely, have you really given your actuaries and data scientists the means to work together to help you gain market share and meet your expectations, thanks to support, methods, collaborative tools and adapted processes?
• Finally, how can you ensure that all your technical teams follow the same processes and methods and are in line with your medium to long-term strategy?

Myth N°6 « The future of Pricing is exclusively through AI and big data based models and insurtechs »

• Would you be prepared to give up all your hard-won experience in favour of completely new methods that you have no experience of?
• Couldn’t you improve your current pricing methods with big data and artificial intelligence by integrating the benefits of data science into your processes and tools?
• Would you risk entrusting the keys to your reactor to the first start-up that comes along? How would you distinguish yourself from the inexperienced insurtechs?