Loss filter

Most contracts cover a specific slice of the cedent’s book — a peril, a geography, a line of business. A filter is the term that selects it: a predicate that keeps only the TELT rows matching the criteria and drops the rest. It is pointwise — each row is evaluated independently, with no state across rows — and it changes which losses are present without transforming their values.

$$\text{filter}(P): \quad L'_{j,t,e} = \begin{cases} L_{j,t,e} & \text{if } P(j,t,e) \text{ is true} \\ \text{excluded} & \text{otherwise} \end{cases}$$

Applied to Trial 9 in isolation, a Florida-hurricane filter — $P = (\text{peril} = \text{HU} \,\wedge\, \text{geography} = \text{FL})$ — keeps four of the thirty occurrences and drops the rest. This is the same selection that opens Contract 1’s dissection:

Earthquake Hurricane

A Florida-hurricane filter on Trial 9: four FL HU occurrences are kept ($216.8M); the other 26 — earthquakes and non-Florida storms — are dropped.

Filter (peril = HU, geography = FL)	Occurrences	Subject
Kept (Florida hurricane)	4	$216.8M
Dropped	26	$55.3M
Trial 9 subject	30	$272.1M

helios_re/loss_filter.py Python

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Across all 20 trials, the filter reduces SunCoast’s full subject to its Florida-hurricane component — the input Contract 1 actually sees:

Loss filter EP curves: SunCoast full subject vs the Florida-hurricane rows only. The filter removes every non-FL-HU loss; the amber curve is what it drops.

Because a filter changes only which rows are present, not their values, it is the natural first term in a pipeline — making explicit the coverage a contract names. Contract 1’s full composition begins with one:

$$\text{Contract 1} = \text{scale}(p) \circ \text{agg}\_{\text{excess}}(0, \ell) \circ \text{occ}\_{\text{excess}}(A, \ell) \circ \text{filter}(\text{HU}, \text{FL})$$

As with every composition, the formula reads right to left — the filter applies first — while data flows left to right. The contract period usually follows the filter, narrowing the kept rows to the coverage window. The CatXoL page assembles the full pipeline.