Insurance
How insurance works, and the finance, marketing, data and AI that run it.
The programs
Five programs, one per domain of the Insurance sector. Every one is readable straight away, without an account, and you can gauge your level on any of them whenever you want.
Insurance
This block builds structural fluency in insurance as an industry: how risk is underwritten, pooled, priced, and paid out across life, P&C, and health lines.
13 lessons · about 3h
- Map the end-to-end insurance value chain and explain how risk flows from policyholder to reinsurer
- Identify the major players in the sector and assess where bargaining power and margin sit across the chain
- Interpret core regulatory requirements (solvency, conduct, capital) and their practical impact on business decisions
Finance for Insurance
Insurance is a finance business wrapped in risk transfer.
26 lessons · about 5h
- Map the end-to-end insurance value chain and explain how risk flows from policyholder to reinsurer
- Identify the major players in the sector and assess where bargaining power and margin sit across the chain
- Interpret core regulatory requirements (solvency, conduct, capital) and their practical impact on business decisions
Marketing for Insurance
Insurance marketing operates under tighter constraints and longer feedback loops than most sectors: policies are sold before value is proven, regulators scrutinize every claim made in advertising, and customer lifetime value depends on renewal behavior that unfolds over years, not weeks.
26 lessons · about 5h
- Map the end-to-end insurance value chain and explain how risk flows from policyholder to reinsurer
- Identify the major players in the sector and assess where bargaining power and margin sit across the chain
- Interpret core regulatory requirements (solvency, conduct, capital) and their practical impact on business decisions
Data for Insurance
This block builds data fluency specific to insurance, where underwriting, pricing, claims and reserving all depend on data quality and structure.
26 lessons · about 5h
- Map the end-to-end insurance value chain and explain how risk flows from policyholder to reinsurer
- Identify the major players in the sector and assess where bargaining power and margin sit across the chain
- Interpret core regulatory requirements (solvency, conduct, capital) and their practical impact on business decisions
AI for Insurance
AI is reshaping insurance across underwriting, pricing, claims and distribution, but the sector's regulatory density and actuarial traditions make adoption distinct from other industries.
26 lessons · about 5h
- Map the end-to-end insurance value chain and explain how risk flows from policyholder to reinsurer
- Identify the major players in the sector and assess where bargaining power and margin sit across the chain
- Interpret core regulatory requirements (solvency, conduct, capital) and their practical impact on business decisions
Insurance is the business of pricing and pooling risk, then paying claims from reserves and investment income. From underwriting to the combined ratio, this vertical gives you the big picture, then finance, marketing, data and AI applied inside it.
Key terms
Why specialize in Insurance?
Cross-cutting skills are not enough in the Insurance sector. It plays by its own rules: a distinct value chain, specific players and balance of power, dense regulation, and key figures you won't find anywhere else. This vertical gives you that sector fluency: first the big picture, then finance, marketing, data and AI applied concretely to Insurance, with the calculations, benchmarks and checklists you actually need on the ground.
What you'll be able to do
- Understand how the Insurance sector works: value chain, key players and balance of power
- Know the major regulations and laws, the sector's acronyms and vocabulary
- Run the key calculations and read the benchmarks specific to Insurance (US and Europe markets)
- Apply finance, marketing, data and AI to the realities of Insurance
- Gauge your level with 5 sector assessments and a competency radar
65 lessons across 16 modules and 5 blocks, with a test per lens and a sector competency radar. Free to read, no account required.
The curriculum in detail
Insurance: how the sector works
Generalhow insurance works: pooling and pricing risk, the underwriting-claims cycle, life vs P&C vs health, reserves and reinsurance, and float.
4 Modules · 13 Lessons
Finance in insurance
Financeinsurance finance: the combined ratio, loss reserves and their uncertainty, the role of investment income and float, and solvency capital.
3 Modules · 13 Lessons
Marketing in insurance
Marketinginsurance marketing: distribution (agents, brokers, direct), trust and claims experience, price comparison, and retention in a low-engagement product.
3 Modules · 13 Lessons
Data in insurance
Datainsurance data: actuarial and claims data, telematics and new risk signals, fraud detection, and the governance and fairness of pricing models.
3 Modules · 13 Lessons
AI in insurance
AIAI in insurance: risk pricing, claims automation and fraud, underwriting assistance, and the fairness/regulatory constraints on models.
3 Modules · 13 Lessons
Prefer to place yourself first?
Five short assessments, one per domain of the Insurance sector. Ten questions each, three minutes, and a competency radar once you have taken more than one.
All sector assessmentsLatest articles
What the blog publishes on Insurance, across every discipline.
- MarketingThe bind rate your PCW traffic never shows youMost direct-to-consumer insurers can tell you their quote volume. Far fewer can tell you why 60-70% of those quotes never convert to a bound policy, or which funnel stage is eating the margin.
- AIDid Blue Cross Blue Shield just prove that hospital AI raises costs by $942M?Blue Cross Blue Shield's claim that hospital AI tools added $942M in spending over two years has handed every CFO a reason to pause. The number deserves scrutiny before it reshapes your capital allocation decisions.
- MarketingOscar Health's Lucie rebrand: what repositioning a health insurance brand actually requiresOscar Health has split its brand architecture into two, launching Lucie for marketplace buyers while refreshing Oscar for its core individual audience. The move offers a precise case study in how to reposition a regulated, low-trust category without erasing the equity you've already built.
- MarketingWinning the price-comparison war and defending policyholder retention in insurancePrice-comparison websites have turned personal lines insurance into a commodity auction, forcing CMOs to compete on margin-destroying premiums or watch policyholders walk at renewal. This article unpacks the mechanics of retention-led marketing in insurance, where the real economic levers sit, and what it actually costs to abandon the fight.
- FinanceBelron's IPO field guide: the people, precedents, and pressure points that define mega-listing readinessBelron's reported exploration of a mega-IPO puts one of Europe's most quietly formidable private businesses under the public-market microscope. This field guide maps the players and precedents that every CFO preparing for a major listing should know cold.
- AIWhere AI bias comes from and how to spot it before it costs youAI bias is not a glitch or an edge case. It is a structural feature of how models are built, and understanding its origins is the first step to catching it before it damages a decision, a product, or a reputation.
Frequently asked questions
What does this insurance vertical actually cover?
It covers five blocks: how insurance works as a business, then finance, marketing, data and AI applied inside the sector. The first block explains risk pooling and pricing, the underwriting-claims cycle, life vs P&C vs health, reserves, reinsurance and float. The four others take the same sector through a specific lens.
Who is this for if I don't work at an insurer?
It works for anyone who deals with insurers without sitting inside one: brokers, insurtech teams, consultants, investors, or a marketing or data lead who just joined the sector. The general block gives you the vocabulary (underwriting, combined ratio, reserves) so the finance and data discussions stop being opaque.
Where should I start: the general block or my own function?
Start with the general block on how insurance works. The finance, marketing, data and AI blocks all assume you know what underwriting, claims and reserves mean, because those mechanics drive the combined ratio, the pricing models and the retention problem alike.
Is there a certificate or diploma at the end?
No. There is no diploma, no state-recognised certification and no affiliation with a school or university. Reading is free and open; an account only saves where you stopped.
What is the combined ratio and why does insurance finance revolve around it?
The combined ratio adds claims costs and expenses as a share of premiums: below 100% the underwriting itself is profitable, above 100% it loses money and has to be covered elsewhere. That elsewhere is investment income on the float, which is why the finance block treats the ratio, loss reserves and solvency capital together rather than separately.
Why is marketing insurance different from marketing most products?
Because it is a low-engagement product bought on price comparison and judged on a claims experience most customers never test. The marketing block therefore focuses on distribution choices (agents, brokers, direct), trust, and retention rather than on classic demand generation.
What is the difference between the data block and the AI block here?
The data block covers the raw material and its governance: actuarial and claims data, telematics and new risk signals, fraud detection, and the fairness of pricing models. The AI block covers what gets built on top: risk pricing, claims automation, underwriting assistance, and the regulatory limits those models run into.
Do the data and AI blocks address pricing fairness and regulation?
Yes, both do, from different angles. The data block treats the governance and fairness of pricing models as part of managing actuarial and claims data; the AI block treats fairness and regulatory constraints as a design limit on risk pricing, claims automation and fraud models.