For Data Scientists & ML Engineers · Insurance Brokers
Insurance Brokers Data for Data Scientists
Insurance Brokers data for data scientists: 10 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
financial time series api for backtesting · alternative data for quantitative research · where to get training data for insurance brokers models
API, files, or your warehouse. Daily, weekly, or hourly.
Which insurance-brokerage datasets should data scientists pull first?
Five of the ten sources clear relevance 2, the gate for this page, and the set averages 8.2 out of 10 on quality against 7.81 for the whole catalog.
Every entry has its own detail record, and the insurance-brokers data hub indexes the vertical end to end, including sources aimed at compliance teams rather than modelers.
How do you build a brokerage-market panel?
County Business Patterns is the structural backbone. Its NAICS 524210 slice runs annually from 1986 through the 2023 reference year (released 26 June 2025), one row per geography-by-industry cell with employment size-class breakdowns, reaching national, all 50 states, counties, MSAs and CSAs, congressional districts and ZIP Codes. Downloads are public-domain CSV - the all-industry county file is 12.7 MB compressed - and disclosure avoidance adds G/H/J noise flags you should carry through as features rather than dropping.
For Europe, EIOPA Insurance Statistics aggregates Solvency II reporting at country level: solo quarters run 2016 Q3 through 2026 Q1, group quarters 2018 Q2 through 2025 Q4, with annual series back to 2016 and legacy 2005-2015 time series alongside. Each row is a country x period x undertaking-type x QRT line-item cell, and the solo quarterly balance-sheet release lands as roughly 21.7 MB of CSV. Both sources.8% of the 1,744 datasets refresh at least weekly and none of these two do.
Which registries give entity-level training signal?
Three registers cover the entity graph. The FCA Financial Services Register holds UK firm-level and individual-level records, including appointed-representative relationships, plus history for formerly authorised firms - useful labels for survival and churn models.
How do you choose between them?
Pick by job. Market-sizing and saturation features: County Business Patterns first, EIOPA aggregates for European coverage. Verification: the NPN lookup.
This page is the insurance-brokers slice of our all data-scientists resources hub, which applies the same rubric to every other industry we cover.
Straight answers
Can I get financial time series for backtesting distribution strategies?
Yes, at aggregate frequency. County Business Patterns supplies annual series back to 1986, and EIOPA publishes quarterly Solvency II aggregates from 2016 Q3 through 2026 Q1 - about 21.7 MB of CSV per solo-quarterly release. Nothing here is intraday; brokerage distribution is a slow-cycle panel problem, not a tick-data one.
Which alternative data suits quantitative research on insurance distribution?
Each tracks distribution capacity rather than sales, which is what alternative-data setups want.
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.
Talk to us