Pricing on rcor

Don’t be mean, be significant!

Sun, 17 May 2026 00:00:00 +0000

Markets do not reward averages. They reward segmentation.

Segmentation should not be confused with separation. The objective is not to isolate risks from one another, nor to discard information through excessive fragmentation. Quite the opposite: meaningful segmentation requires integrating all available information in order to explain not only the expected value, but also the structure of volatility surrounding it.

In actuarial science, the temptation to remain close to the market mean is almost structural. Benchmarking exercises, peer comparisons, regulatory pressure, rating and audit constraints, and committee-driven governance all pull insurers toward the same gravitational center: the comfort of being approximately identical.

Better than AI

Wed, 19 Jun 2024 00:00:00 +0000

AI, including large language models (LLMs), excel at composing poems. The distinction between AI and a conventional algorithm lies in our understanding of the solutions they propose—when we fully understand the solution, it is merely an algorithm.

If one utilizes advanced AI for setting pricing—given that an unbiased solution is provided, but is not fully understood by the user—they risk not comprehending the rationale behind the proposed segmentation.

Without understanding the reasoning behind the optimal segmentation, a business lacks a strategy and therefore has no competitive advantage.

Trading volatility

Mon, 15 Jan 2018 00:00:00 +0000

Similar to Darwin, we view variance as the true reality, while the average is a more abstract concept.

Embracing variance is challenging. Everyone wants to make money without risk, yet without volatility, there’s nothing to gain.

Yet, nearly everyone forecasts the next expected value, not the evolving variance. The same variance can yield different means, and the same expected value can result from distinct variances.

Our pricing model prioritizes capturing the variance first, and, given that variance, seeks to explain the expected value.