The Machine Era of Insurance Compliance
Something fundamental shifted in 2025. Walk into any insurer’s compliance department and you’ll notice it. The coffee’s gone cold. The spreadsheets have multiplied. And somewhere between the Consumer Duty reports and the latest FCA market study, everyone’s doing the same mental arithmetic: how do you prove fairness across 50,000 claims when your QA team can only review 200?
You can’t. That’s the uncomfortable truth regulators are now saying out loud.
By 2026, compliance will run on proof, not promises. The FCA wants evidence tied to real customer journeys. They want data that shows how your claims operation actually performs, not a carefully curated sample that shows how it sometimes performs. And they’re done waiting.
Consumer Duty moves from planning to proof
The Consumer Duty stopped being about preparation somewhere around mid-2025. 2025 and 2026 mark what the FCA calls the “showing your work” phase. For general insurance and claims, that means connecting policy intentions to actual outcomes. Every single time.
Three areas dominate the regulatory radar:
Products and services must deliver what customers reasonably expect during a claim. Not in the brochure. In practice.
Price and value calculations now require data on claim frequencies, acceptance rates, settlements, complaints and the actual worth of any add-ons you’re selling. The FCA’s scrutiny of multi-occupancy building insurance set a new standard here. They wanted loss ratios, remuneration structures, profitability metrics. Building by building. That expectation now applies across the market.
Customer support needs to be fast, fair and properly equipped to help vulnerable customers. Same standards. Same treatment. Provable.
Traditional QA runs on samples and checklists. That may have worked in a simpler regulatory era, but it doesn’t work now.
The Fair Value Calculation Has Changed
Fair value used to be an annual exercise. Sign off, file away, repeat next year. Those days are gone. Current expectations from the FCA require continuous monitoring of who’s buying policies, who’s claiming, who’s complaining, and which customer segments consistently get worse outcomes.
This level of granularity demands:
- Data linking pricing, distribution, claims handling and complaint patterns
- Visibility into segments with poorer outcomes
- Evidence explaining the performance gaps
Sampling 2% of your interactions won’t cut it when regulators and consumer groups are analysing market-wide data. They can see patterns you can’t. And they’re asking why you can’t see them too.
Discover how Aveni Detect enables 100% claims oversight without expanding QA teams →
The Compliance Spotlight Intensifies
ICOBS has always required prompt, fair claims handling. Recent enforcement activity suggests the FCA’s patience for slow settlements, weak offers and poor complaints management has expired.
Add to that the vulnerability guidance. Vulnerable customers must receive comparable outcomes to everyone else. Not sympathetic noises. Actual outcomes. Consumer groups now track rejection rates, policy clarity, service consistency. They’re using the same value measures data the FCA requires from you.
When external watchdogs have better data visibility than your internal teams, you’ve got a structural problem.
Why Traditional QA Can’t Scale
Modern claims don’t happen in one place. A single claim touches:
- Initial notification via phone, app, or web form
- Vendor coordination, adjuster visits, repair network communications
- Internal fraud checks, underwriting reviews
- Decision letters, settlement calls, potential appeals
These journeys span teams, systems, channels. Product governance and fair value rules require you to understand end-to-end performance. Traditional QA gives you snapshots.
Small sample checks catch obvious failures. They don’t surface patterns. They can’t identify which distribution partners, regions or customer types correlate with higher rejection rates. They won’t tell you if lengthy claims durations predict complaints six weeks later.
See how continuous monitoring reveals hidden compliance risks across claim types →
The Vulnerability Problem Gets Worse
The FCA’s vulnerability guidance emphasises tone, clarity, appropriate adjustments. Checklists confirm script adherence. They don’t capture how the conversation actually felt to the customer. They can’t measure whether agents consistently recognise vulnerability indicators across different scenarios.
Market data shows wide variation in claims acceptance and complaint rates. That variation creates pressure to evidence consistency. Sampling won’t provide that evidence.
What 2026 Compliance Actually Requires
Meeting next year’s expectations demands three capabilities most insurers don’t currently have.
Complete Coverage
Boards, auditors and regulators want proof that conduct and value risks receive monitoring across all interactions. Not 5%. Not 10%. All of them. Particularly high-risk areas like complex claims, vulnerable customers, contested decisions.
This requires systematic, data-driven oversight. Across every call, transcript, document.
Connected Evidence
Fair value reviews and Consumer Duty assessments need data showing:
- How claims handling affects value delivery
- How claims and complaints data inform product governance
- Whether operational changes actually improved outcomes
QA must feed structured, actionable data into pricing reviews, distribution oversight, governance processes.
Vulnerability at Scale
You need clear trigger definitions, scalable detection mechanisms, and evidence that vulnerable customers receive appropriate support and comparable outcomes. Traditional QA supports this work. It can’t carry it alone.
Learn why leading insurers are moving beyond sampling-based compliance models →
Enter the Machine Line of Defence
Traditional QA was built for a smaller, simpler regulatory world. Insurance compliance in 2026 operates at industrial scale with surgical precision requirements. That’s why insurers are adopting what Aveni calls the Machine Line of Defence.
It works alongside the existing three lines of defence. It provides systematic oversight across every claim interaction, reviews complete customer journeys, detects indicators tied to regulatory requirements, and generates structured data that supports fair value assessments, product governance and Consumer Duty compliance.
Aveni Detect helps teams focus expert judgement where it matters most, backed by complete coverage and consistent monitoring. Firms that adopt this machine-supported assurance model will face the next fair value review, Consumer Duty assessment or FCA market study with evidence, not excuses.
The regulatory reckoning isn’t coming. It’s here. The question isn’t whether you’ll need to prove compliance across all your claims. The question is whether you’ll have the infrastructure to do it.
Start building the assurance model 2026 expects → Book a demo
