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Loss Ratio | The Structure View
In the previous edition, we looked at how a portfolio appears from a capital perspective - combining performance, scale, and volatility into a single view. It still assumes the portfolio risk sits with us. Reinsurance reshapes the loss distribution, often in ways that are not visible in standard performance views. Two portfolios with identical net loss ratios can carry very different retained risk. Without understanding that structure, we are not seeing the risk we ultimately
Rika Taute
Apr 25


Loss Ratio | The Capital View
In the last two editions, we improved how loss ratios are understood - first by testing whether they are credible, and then by identifying what is driving them. It provided a clearer view of internal performance. But capital is not assessing internal performance. Capital providers are asking what risk they are backing - and whether it justifies the capital. The way most portfolios are presented does not answer that question. The Decision Question Is the portfolio attractive
Rika Taute
Apr 9


Week 2 of Loss Ratio | Looking Beyond the Default
The Decision Question Week 1 answered: Are we performing above or below expectation? The next natural question is: What is driving the change in loss ratios? Loss ratios can move because of: claim frequency claim severity large losses Without separating these drivers, pricing decisions risk targeting the wrong lever . A loss ratio of 75% can mean very different things: Stable, credible portfolio Large exposure base, predictable development, stable frequency → 75% is informa
Rika Taute
Mar 26


Week 1 of Loss Ratio | The Default View
How underwriting performance is typically presented The Decision Question Is the portfolio performing in line with the priced expectation, and is the signal credible enough to act on? Underwriting committees usually want to know: Are we above or below target? Is it material enough to matter? Is the signal reliable or noise? Default Practice The most common chart (shown per product / line): Single line of ultimate loss ratio over time vs a target or benchmark. Why this works:
Rika Taute
Mar 13
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