Building a bridge between pricing and reserving
Pricing and reserving often follow separate paths, each with their own data, methods, assumptions and objectives. While both deliver value independently, their full potential is unlocked when they work together. Pricing methodologies and granular insights can refine reserving assumptions when the portfolio mix is evolving, while reserving’s long-term perspective strengthens pricing decisions. Here, I explore how bridging non-life pricing and reserving can lead to more accurate models, better informed decision making and stronger financial outcomes.
Breaking down silos
When analysing written business, claim reserves can be categorised into various components: future claims from written business, unreported claims, attritional claims expected to settle at zero, attritional non-zero claims progressing to full settlement, attritional claims that may develop into large losses, and large claim movements.
In reserving, these are typically modelled using accident-year triangles – separating attritional and large claims – and applying loss ratio proxies to unearned premium segments. In pricing, frequency models often exclude the latest accident months, while severity models rely on loss development factors (LDFs) to account for claim maturation. Can we go beyond these siloed approaches? Is there a more integrated way to model claims, enhancing both pricing and reserving – and thus leading to better insights and decision making?
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