---
title: "InsurTech"
url: "https://mahouse.world/glossary/insurtech"
type: "glossary term"
topic: "Fintech markets"
author: "Ihor Vlasov"
reviewed: "2026-09-20"
published: "2026-08-06"
---

# InsurTech

InsurTech is technology applied to insurance — distribution, underwriting, pricing, policy administration and claims — by new entrants and by incumbents rebuilding those functions: digital brokers and comparison platforms, full-stack digital insurers with their own licences, managing general agents writing on a carrier's capital, embedded insurance sold inside other products, and software and data vendors serving all of them. The 2021 IPO cohort's collapse taught the sector that software does not change loss ratios by itself.

**Also known as:** Insurance technology, Insurtech, Digital insurance, Embedded insurance

## Key facts

- **Used at:** personal lines (motor, home, renters, pet, travel), health and life, small-business insurance, embedded insurance (with tickets, devices, rentals, gig work), and the insurance value chain's software — quoting, policy administration, claims automation, fraud, and the data and AI behind pricing
- **Binding:** insurance is regulated by carrier licence (capital under Solvency II in the EU, state regulation in the US, the PRA and FCA in the UK); intermediaries need broker or agent authorisation; MGAs write under a carrier's licence and its oversight; pricing rules (the UK's ban on price walking, US state rate filings) and anti-discrimination rules constrain what data and models may do; the EU AI Act classes life and health insurance pricing and risk assessment as high-risk
- **Typical models:** digital broker or aggregator (commission, no risk); MGA (commission and profit share, underwriting on a carrier's capital); full-stack carrier (premium, claims and capital — the hardest); embedded (a distribution partner and a carrier or MGA); enabler (software and data sold to insurers)
- **Typical economics:** brokers and enablers at software-like margins; MGAs dependent on the loss ratio and the carrier's appetite; full-stack insurers judged on combined ratio (losses plus expenses over premium), where the 2021 cohort ran above 100% for years while growing; embedded insurance at attach rates of 5–30% and commissions of 10–30%
- **Parties:** the insurtechs, the carriers and reinsurers whose capital most of them use, the regulators, the distribution partners for embedded products, and the acquirers — carriers, brokers and private equity

## How InsurTech works in practice

A startup wants to sell renters' insurance to a generation that will not call a broker. Route one: become a digital broker, quote from several carriers, earn commission, carry no risk — a marketing business with thin margins. Route two: become an MGA, design the product, price it with the startup's own model, sell it under its brand, and write it on a carrier's licence and capital with a profit share — the startup owns the customer and the product and shares the risk. Route three: raise capital, obtain a licence, become a carrier — the startup keeps all the economics and bears all the losses, with a capital requirement that grows with premium. The 2021 IPO cohort chose route three and discovered that a good app does not lower claims; combined ratios above 120% consumed the capital raised and the shares fell 80–90%. The MGAs and the brokers with the same technology survived on other people's balance sheets.

Where technology changes the economics is specific. Pricing models with new data (telematics for motor, IoT for property, health data for life) can select risk better — within the rules on what data may be used and the fairness constraints regulators enforce. Claims automation cuts expense and cycle time — a photo-based motor claim settled in hours — and fraud models cut leakage. Embedded distribution reaches customers at the moment of need at low acquisition cost. And the enablers selling these to incumbents have the most reliable business of all. The M&A market reflects it: carriers buy MGAs and enablers to acquire capability; brokers consolidate digital distribution; private equity buys the software; and the full-stack survivors are valued on their combined ratio, like any insurer.

For a founder, insurtech is a choice of how much balance sheet to own, and the answer that worked was "as little as possible until the loss ratio is proven"; for a buyer, it is distribution, capability or a book, each priced differently.

## Example

An embedded-insurance MGA sells device protection through 40 electronics retailers and telcos: the customer is offered cover at checkout, the MGA prices and administers the policy, a carrier bears the risk and takes 60% of the premium, and the MGA earns the rest and a profit share if the loss ratio beats a target. Year four: €90 million of gross written premium, attach rate 18% on partnered checkouts, loss ratio 52% against a 60% target, MGA revenue €34 million, EBITDA €9 million. A large insurer buys the MGA for €220 million — 6.5× revenue, 24× EBITDA — for the distribution agreements, the pricing model and the claims platform, and moves the risk onto its own paper, capturing the carrier's 60% as well. The MGA's founders, who had declined a full-stack licence in year two on the advice of their reinsurer, receive the price a carrier would not have paid for a loss-making insurer.

## Common mistakes

- Going full-stack before the loss ratio is proven on someone else's capital.
- Reading growth in premium as success; the combined ratio is the measure.
- Pricing models on data the rules do not allow or fairness tests would fail.
- Embedded products with attach rates that only a subsidy sustains.
- Valuing an insurtech on technology when the carrier relationships and the loss ratio are the asset.

## Related terms

- [Fintech](https://mahouse.world/glossary/fintech.md)
- [Embedded Finance](https://mahouse.world/glossary/embedded-finance.md)
- [WealthTech](https://mahouse.world/glossary/wealthtech.md)
- [Initial Public Offering](https://mahouse.world/glossary/initial-public-offering.md)
- [AI Bias](https://mahouse.world/glossary/ai-bias.md)
- [Explainable AI](https://mahouse.world/glossary/explainable-ai.md)

— Ihor Vlasov, M&A FinTech Expert. Last reviewed 2026-09-20. https://mahouse.world/authors/ihor-vlasov