How Data Scientists Use Reinsurance Data

OECD Insurance Market Indicators (INSIND - Premiums, Penetration, Density) is the only source here built like a research panel: 31,560 observations covering 71 OECD member and partner countries from 1983 to 2022, split by Total, Life and Non-Life segments. Indicators include gross premiums, penetration, density, market share, retention ratio and ratio of reinsurance accepted - the last two are direct targets for cession-appetite models. A composite key of country, segment and year reconstructs the panel deterministically, and the labelled export runs 4.8 MB as CSV.

Access is the differentiator worth automating around: INSIND streams through the SDMX API without authentication, so the same query runs identically on a laptop and in CI, and an annual refresh never breaks a token. For treaty-sizing work, pair retention ratios with penetration and density to rank candidate territories by ceding propensity and premium depth at once. One caveat for modellers: coverage ends at 2022, so recent-cycle features need a supplement rather than extrapolation.

Set expectations before training anything: seven annual observations per class make these prototyping panels, not deep-learning corpora. Their real value is structural - ready-made tidy examples of frontier-market reinsurance dependence, useful for pipeline demonstrations, imputation walkthroughs or classroom exercises where the whole dataset fits on one screen.

A working stack takes an afternoon if you sequence the eight datasets by dependency rather than by quality score:

1. Pull the panel first. Query OECD INSIND over SDMX for all 71 countries, 1983-2022, and land it as a country-segment-year table - no authentication means the call is reproducible in CI from day one.

1. Attach the cycle series. Append quarterly KPIs from Lloyd's Market Financial Performance & Syndicate Reports - GWP, combined ratio, profit before tax - so every observation carries a market-state timestamp.

1. Add national depth last. Parse APRA's XLSX databases down to individual authorised general insurers (December 2002 onward) and extract IRDAI's obligatory-cession tables from its FY2006-07 to 2024-25 PDFs only for the territories your models actually cover.

1. Finish with the tidy panels. Read both Uganda Parquet files for frontier-market test cases - they are small enough to embed as fixtures in unit tests.

Start with the reinsurance data guide - the pillar post that maps all eight free reinsurance datasets and shows where catastrophe-loss, market-structure and regulatory sources interlock. For the same pool ranked specifically for modeling work, open reinsurance data for data scientists; for the cession-side analysis built on INSIND, read Reinsurance Retention Ratio by Country: Free Data Sources.