Supervising the Chain Ladder

The paper proposes treating the chain ladder’s pattern selection as a supervised‑learning problem, framing adjustments as penalties and hyperparameters on a convex loss function. It introduces generalized data weights with decay and power parameters, benchmark shaping, and Whittaker‑Henderson smoothing, all combined into a single linear system for minimisation. The authors demonstrate the approach with a real Schedule P triangle, showing how experience‑based and prospective adjustments can be objectively set via a training loop and reserve validation.

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• benchmarks