Where chain meets cover
Six insurance use cases, one honest framework to judge them, and the discipline to kill the ones that don't clear the bar. Featuring the audit-ledger pattern that actually shipped — in the platform this course uses as its case study.
L1The value framework — three questions before any chain
Ask, in order: (1) Do multiple organisations need to write to shared state? (2) Is there a trust or reconciliation gap between them that a single operator can't (or shouldn't) fill? (3) Are the rules automatable — decidable from data the parties accept? Only "yes, yes, yes" earns a ledger.
L2Use case 1 · Parametric insurance (the star)
Parametric cover pays a pre-agreed amount when a measurable index crosses a threshold — wind speed ≥ 200 km/h within 50 km, rainfall < 100 mm in a season, flight delayed ≥ 3 h — no loss adjustment at all.
L3Use case 2 · Fraud & shared registries
Insurance fraud exceeds $40 B/yr in the US alone (industry estimates, non-health P&C). A big slice is cross-insurer: the same car crash claimed at three carriers, the same policy double-financed. Individually invisible; jointly obvious.
- Design: a consortium registry of privacy-preserving claim fingerprints (hashes of normalized VIN + date + parties). A new claim hashes in; a match anywhere raises a flag. No raw PII shared — the M01 hash-commitment trick at industry scale.
- Why a ledger and not a bureau? Sometimes a bureau is fine (that's the honest answer). The chain earns its place when insurers won't hand a competitor-visible dataset to any single operator, and when regulators want tamper-evident audit of who queried what.
L4Use case 3 · KYC & identity reuse
Every insurer re-verifies the same customer the bank verified last month. Consortium KYC lets institution A attest "verified to standard X on date D" — an attestation reusable by B with consent, cutting onboarding cost and friction. Add decentralised identifiers (DIDs) and verifiable credentials and the customer can carry the attestation. Galileo's ecosystem already routes KYC via Onfido; an attestation layer would sit exactly there. Hard part (always): liability when a reused KYC turns out wrong — a legal-ring question, not a technical one.
L5Use case 4 · Subrogation netting — the quiet success
State Farm ↔ USAA (production Dec 2020)
shippedAuto insurers constantly owe each other subrogation recoveries: A pays its customer, then recovers from B (the at-fault side's insurer). Between two giants that's thousands of cheques and demand letters monthly — in both directions.
The build: a shared permissioned ledger records each demand and payment; instead of settling claim-by-claim, the net balance settles periodically in one payment. Forbes reported the 2019 pilot showing a "stark increase in efficiency"; full automation was reached in December 2020.
The lesson: tightly-scoped, two-party, high-volume, low-drama reconciliation — no token, no press-cycle economics — is exactly the profile that survives contact with production.
L6Use case 5 · Reinsurance placement & settlement
The strongest structural fit (M04 L5) — treaty terms, cessions, and cat-event settlements shared by insurer, broker, and a tower of reinsurers. B3i shipped a live Cat XoL placement product on Corda… and still died in 2022 when funding collapsed. Both facts matter: the workflow fit is real (RiskStream continues the work), and the consortium economics are the actual boss fight. Full autopsy in M08.
L7Use case 6 · The audit ledger — Galileo, the shipped case study
Pattern: every write command (create policy, update party, record payment) goes to a private blockchain first; Kafka streams accepted ledger entries to workers that update MongoDB read models; current vs archive collections preserve every version. The chain is the book-keeper; the database is a disposable projection.
What it buys: regulator-grade tamper evidence, total ordering of business events, rebuildable state, and decades-long record retention by construction (M04 L6). What it costs: infrastructure (3 chain nodes + 12 workers), eventual consistency between write and read, and scarce expertise — costs that contributed to the replatforming decision.
Honest framing: with one operator, a signed append-only log (or QLDB-style ledger database) could deliver much of this. The blockchain form pays off if third parties — auditors, regulators, partners — ever get verification access. That upgrade path is the capstone's opening question.
L8Anti-patterns — how to kill a use case in one meeting
| Anti-pattern | Smell | The question that kills it |
|---|---|---|
| Blockchain-as-database | One writer, internal data, no external verifier | "Who, outside us, ever checks this ledger?" |
| Tech-first consortium | Platform chosen before the business case; members join for optics | "Who pays the run costs in year 4?" (see B3i, we.trade) |
| Oracle hand-waving | Parametric deck with no data-governance slide | "Who runs the nodes, and what happens when sources disagree mid-typhoon?" |
| PII on-chain | Customer records "immutably stored" | "Show me the erasure-request procedure." |
| Token where none is needed | A utility token bolted onto a B2B workflow | "Does removing the token break anything except the pitch?" |
Exercise 5.1 · Score six, kill three
framework practiceRun each candidate through the three framework questions and Fig 5.1. Defend your kills in one sentence each — then compare with our verdicts (flip below).
- Typhoon parametric for GCash users
- Internal policy-document archive "on blockchain"
- Five-insurer motor fraud registry
- Blockchain loyalty points for agents
- Insurer↔reinsurer treaty cession ledger
- "NFT policies" for a single carrier's retail app
Our verdicts
Keep: 1 (multi-party trust via oracle + instant payout is the product), 3 (competitors, shared writes, privacy via hashes), 5 (chronic multi-party reconciliation). Kill: 2 (one writer, no external verifier — use WORM storage/signed logs), 4 (a database with marketing), 6 (single carrier ⇒ the NFT adds transfer complexity nobody asked for — unless cover portability between platforms is truly the product).
Sources: State Farm newsroom & Forbes (2019–20); Etherisc/Lemonade figures as cited in M08; US fraud estimate per industry bodies (as of 2025); Galileo dossier for the case file.