For Data Scientists & ML Engineers · Reinsurance
Reinsurance Data for Data Scientists
Reinsurance data for data scientists starts with three relevance-3 sources: NOAA NCEI's billion-dollar disaster record for catastrophe features, APRA's National Claims and Policies Database for long-tail claim development, and OECD INSIND's premium panels over an unauthenticated SDMX API.
financial time series api for backtesting · alternative data for quantitative research · where to get training data for reinsurance models
API, files, or your warehouse. Daily, weekly, or hourly.
What can you actually train on?
APRA National Claims and Policies Database (NCPD) is the claims-development table: masked policy-and-claim cells going back to 2003, cut by product, industry occupation, deductible band and limit band. That masking makes it one of the few open sources where triangle-style long-tail development modeling is possible without a carrier data feed.
Format coverage is thinner than the catalog norm here: only 2 of these 10 datasets offer CSV and only 1 offers JSON, whereas catalog-wide CSV ships with 734 of 1,744 datasets (42.1%) and JSON with 675 (38.7%).
How do you turn regulator and syndicate documents into training tables?
Four sources require document processing before they are features. APRA Statistics Portal indexes 75+ APRA statistical publications across five industries, monthly, as the discovery layer for the right XLSX series.
The genuine extraction jobs are IRDAI Annual Reports (India Insurance & Reinsurance Statistics), where tables live inside report PDFs up to 70MB with no machine-readable alternative, and Lloyd's Market Financial Performance & Syndicate Reports, where syndicate SFCR filings and market results files parse into panels of combined ratios and premium growth.
How do you choose between them?
Pick by job. Catastrophe features: NOAA NCEI first, joined on event geography and year. Claims development triangles: APRA NCPD's masked cells. Macro priors and pricing context: OECD INSIND. Network analysis of who reinsures whom: the Lloyd's Market Directory. Document-mining benchmarks for OCR and table extraction models: IRDAI reports and Lloyd's syndicate SFCRs.
This page is the reinsurance slice of our all data-scientists resources hub, which applies the same rubric to every other industry we cover, alongside the reinsurance data hub for industry-level context.
Straight answers
Is there a reinsurance time series API suitable for backtesting-style workflows?
Yes. Both Uganda outgoings panels also serve tidy Parquet via API for pipeline tests.
What alternative data suits quantitative research on reinsurance?
Non-carrier signals: NOAA NCEI's event-level billion-dollar disaster record as exogenous catastrophe features, Lloyd's Market Directory entity-resolved into a managing-agent/syndicate/coverholder graph, and Lloyd's syndicate SFCR filings parsed into combined-ratio panels. Each moves independently of regulator aggregates.
Where can I get training data for catastrophe and claims-development models?
Join two sources: NOAA NCEI supplies event-level billion-dollar disasters since 1980 as CSV, JSON or XML with a stable DOI, and APRA's National Claims and Policies Database contributes masked claim cells from 2003 onward segmented by product, industry, deductible and limit bands - enough structure for development-triangle features.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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