InsurancePY Applied — Standalone

## The scenario CubedNet Insurance's Q3 board pack flagged that the motor book's **loss ratio drifted from 62% to 71% over twelve months**, with broker-channel business contributing disproportionately. You have been asked to (a) decompose the loss-ratio movement by channel × cover type × incident category, (b) compare case-reserve set on day one against the eventual paid amount, and (c) identify any FNOL cohort where the reserve adequacy ratio is materially off. You receive twelve months of FNOLs, payment transactions (initial + supplementary), reserve history (case reserve revisions), policy/channel reference, and the cover-rules history. ## Deliverables 1. `loss_ratio_decomposition.csv` — month × channel × cover × incident, with earned premium, paid + outstanding loss, and the loss ratio. Aggregate up the dimension tree (channel total, cover total, grand total) for sense-checking. 2. `reserve_adequacy.csv` — for FNOLs at least 9 months old: initial case reserve, ultimate paid, ratio, plus a triangle of paid-to-date by development month. 3. `cohort_alerts.csv` — cohorts whose reserve adequacy ratio is more than 1.5σ from the book mean. Use enough exposure (≥30 FNOLs) to avoid noise. 4. `findings.md` — 500-700 words explaining what's actually driving the drift and which assumptions you'd push back on. ## Acceptance criteria (summary) pandas/numpy allowed · earned-premium calculation is time-weighted (a policy mid-month earns ½ of monthly premium) · supplementary payments handled · ≥3 charts checked in · findings.md substantive and specific · reproducible · ≥10 conventional commits. Full brief, dataset orientation, and starter notebook appear inside the lesson once enrolled.