Insurance Does Not Have an AI Problem. It Has a Proof Problem
Good Proof·25 February 2026·9 min read
Insurance teams are moving fast on AI, but the real bottleneck is no longer model performance. It is whether a firm can prove what was decided, under what policy, with what authority, and whether that reliance should still stand when a complaint, regulator, or audit arrives months later.
Insurance is not waiting for AI anymore
The insurance sector is already past the experimentation headline.
EIOPA reports that AI is already being used across the insurance value chain, with 50% of non life insurers and 24% of life insurers using AI in areas such as pricing and underwriting, fraud detection, and claims management. EIOPA also reports that GenAI adoption is now widespread, with nearly two thirds of insurers already using it, while many remain at proof of concept stage and are rolling it out cautiously.
The UK picture is similar in intensity. The UK Treasury Committee noted that 75% of financial services firms are now using AI, with the highest take up among insurers and international banks.
So the strategic question for insurance is no longer whether AI is coming.
It is this:
How do you scale AI decisions without scaling complaints, disputes, and regulatory exposure at the same time?
That is where most programmes get stuck.
Not because the model is bad.
Because the proof is weak.
The trust gap is already visible
There is a reason this matters now.
EIOPA has been explicit that data privacy, security, and ethical use are trust issues, not just technical issues. In one of its recent supervisory speeches, it highlighted that 24% of EU consumers do not trust insurers to collect and use their personal data in an ethical way.
That number is commercially important.
When trust is fragile, every automated decision gets a shorter fuse.
A pricing decision that looks efficient internally can become a reputational problem externally.
A claims workflow that looks streamlined in a demo can become a complaint escalation if nobody can clearly explain:
what was decided
what data or status it relied on
which policy version applied
whether the action was within scope
who approved that class of action
whether the conditions changed later
Most insurers can answer some of that.
Very few can answer all of it quickly, consistently, and in a way that survives scrutiny across functions.
Regulation is making the same point in different languages
Europe and the UK are not using identical rulebooks, but they are converging on the same operational demand.
In the EU, high impact AI use in insurance is explicitly in scope
Under Annex III of the EU AI Act, AI systems used for risk assessment and pricing for natural persons in life and health insurance are listed as high risk uses. The Act also explains why, noting these uses can significantly affect livelihoods and can contribute to exclusion or discrimination if not properly designed and used.
The implementation timeline has become more dynamic due to standards delays and the Digital Omnibus proposal, but the direction is unchanged. The Commission confirms phased obligations are already in motion and that high risk timing remains a live implementation issue, not a theoretical one.
In practice, that means insurance teams cannot wait for perfect clarity before building evidence discipline.
DORA raises the bar on resilience and third party dependence
DORA now applies across the EU financial sector, including insurers. EIOPA describes it as the framework to strengthen digital operational resilience, covering ICT risk management, third party risk, testing, incident handling, and related controls. It also highlights DORA oversight for critical ICT third party providers, specifically to address concentration and systemic dependency risks.
This matters because many insurance AI workflows now depend on vendors, cloud services, model providers, and external tooling.
If a decision chain spans internal and external systems, then proof of reliance becomes more important, not less.
In the UK, Consumer Duty turns evidence into a frontline issue
The FCA continues to treat Consumer Duty as a core priority and is explicit that firms need to embed it, evidence outcomes, and learn from published examples of good and poor practice. It also emphasises a preference for relying on the Duty rather than adding endless prescriptive rules.
That sounds flexible, but it creates a harder operational burden.
Flexibility only works if you can evidence why your approach is reasonable.
The FCA insurance outcomes monitoring review makes this plain. It states firms must regularly assess, test, understand, and evidence customer outcomes, and reviewed board or committee reporting from larger insurers, intermediaries, and regulated third party outsourcers to test how firms were monitoring and acting on poor outcomes.
The FCA insurance priorities report also points to continued scrutiny of claims handling oversight, including outsourcers and delegated authority models.
In other words, the market is moving toward the same requirement from multiple directions:
Do not just automate decisions. Make them defensible.
The expensive problem in insurance is not model error alone
Insurance firms already spend real money on the consequences of weak decision evidence.
Look at fraud pressure alone.
The ABI reports that insurers detected £1.16 billion of fraudulent general insurance claims in 2024, with 98,400 fraud related claims identified, and motor remaining the largest category.
That does not mean more automation is the problem.
It means automation without reliable proof creates a second cost layer:
internal disputes about what happened
complaints handling drag
audit reconstruction work
legal review time
third party challenge cycles
regulator response time
governance meetings that exist only because evidence is fragmented
Most organisations describe these as separate problems.
They are usually the same problem in different outfits.
A team is trying to reconstruct a high impact decision after the fact, from incomplete records, across multiple systems, under time pressure.
That is where Good Proof™ creates leverage.
Why AI agents make this more urgent
GenAI and agentic workflows change the economics of action.
They do not only make software cheaper to build.
They make decisions and decision-like actions cheaper to trigger.
That is the opportunity and the risk.
In insurance, the next wave is not just chat assistants.
It is increasingly automated operational flows around:
claims triage
fraud escalation
document handling
underwriting support
customer vulnerability support
service routing
policy servicing actions
internal analyst workflows
Each of those touches a decision surface.
The question that follows is not only whether the model was accurate.
It is whether the action was:
authorised
within scope
tied to the right policy and version
supported by valid conditions at the time
reviewable later
stoppable if the conditions changed
This is exactly where many AI governance programmes become too abstract.
They focus on principles and inventories.
Those matter.
But when the complaint lands or the audit request arrives, the operational question is brutally concrete.
Show me what this action relied on, and show me whether reliance was still valid.
What Good Proof™ changes in practice
Good Proof™ is not another AI model governance deck.
It is a decision evidence layer for high impact actions.
It turns a hard to reconstruct event into a portable proof record that can be checked later without exposing the full internal payload.
That matters because insurers do not only need transparency.
They need safe transparency.
It proves reliance, not just output
Most systems can show an output.
Some systems can show logs.
Good Proof™ is designed to show the thing institutions actually need to defend:
what action occurred
what status or evidence was relied on
what scope and authority applied
what policy version governed it
when the reliance was valid
whether reliance was later revoked or changed
That is a different category of value.
It is the difference between a technical record and a defensible decision record.
It makes revocation operational, not theoretical
Insurance risk is dynamic.
A condition can change after the initial action.
A dependency can fail.
A policy can be updated.
A third party status can be withdrawn.
A control can go out of date.
Most firms discover this too late because their evidence is static.
Good Proof™ is built for a world where status matters after the decision, not only at the moment of execution.
That helps teams stop relying on stale assumptions before a minor issue becomes a major one.
It reduces oversharing while improving assurance
Insurance teams often face a false choice.
Either share too much internal detail to prove a point, or share too little and trigger mistrust.
Good Proof™ is useful because it focuses on proof, not payloads.
That means teams can demonstrate decision integrity and governance posture without exposing every internal system detail, model parameter, or proprietary process.
For regulated environments and multi party ecosystems, that is a major commercial advantage.
It helps align cross functional teams around the same evidence
AI governance, risk, legal, compliance, operations, claims, fraud, and procurement often review the same event through different lenses.
That is normal.
What slows everything down is when each function sees a different version of the truth.
Good Proof™ gives those teams a shared, verifiable decision record.
That does not eliminate debate.
It eliminates unnecessary debate.
And that is where time and cost are usually lost.
Why insurers will pay for this now
The budget for this does not need a new category.
It is already spread across pain that insurers are actively funding today.
The spend already exists in:
complaints and remediation operations
fraud controls and investigations
AI governance and compliance rollout
DORA and resilience programmes
third party oversight
audit and assurance
legal and regulatory response work
Consumer Duty outcomes monitoring
delegated authority and outsourcer oversight
Good Proof™ is valuable because it compresses effort across all of them.
It makes several expensive activities partially redundant:
manual evidence reconstruction
repeated cross team fact finding
screenshot based audit trails
static approvals that cannot prove current validity
long review cycles caused by missing decision context
oversharing internal payloads to prove a narrow point
That is why this is not a nice to have governance layer.
It is a cost reducer and trust multiplier inside budgets that already exist.
The strategic upside is bigger than compliance
The obvious value is auditability and control.
The bigger value is speed with permission.
Insurance firms are not short of AI ideas.
They are short of mechanisms that let risk, legal, operations, and leadership say yes with confidence.
When that confidence exists, firms can move faster on:
AI supported claims workflows
more responsive fraud operations
better customer support automation
controlled GenAI deployment
cross border governance consistency
vendor and third party integration
When that confidence does not exist, everything slows down and every pilot stays trapped in committee.
This is the quiet commercial role of Good Proof™.
It does not replace innovation.
It makes innovation easier to approve, safer to scale, and cheaper to defend later.
The real insurance moat in the AI era
As AI lowers the cost of building features, the scarce asset is no longer the interface.
It is the ability to create defensible reliance.
Insurance will continue to adopt AI quickly because the efficiency pressure is real and the use cases are obvious. EIOPA already shows the adoption curve, and regulators are already signalling the standards for trust, resilience, and evidence.
The firms that win will not only have better models.
They will have better proof.
And in insurance, that is usually the difference between a clever pilot and a system the business can actually rely on.
