AI for claims processing,
governed properly.
AI is reshaping how UK insurers handle claims, from first notification of loss to settlement. Done well it settles fair claims faster and frees people for the hard ones. The Institute of AI helps you capture that value without cutting corners on fairness, explainability, or the regulator's expectations.
A real use case, not a gamble.
Claims is the moment an insurance promise is tested. It is also the most labour-intensive, data-heavy part of the business, which is why UK insurers are moving first here on artificial intelligence: triaging first notification of loss, reading unstructured documents, spotting likely fraud, and steering routine claims through settlement faster. The prize is real, a lower cost of handling and a better experience at the worst moment of a customer relationship.
The exposure is real too. A claims decision decides whether a household or a business is made whole. Get the model wrong and you decline a valid claim, underpay a vulnerable policyholder, or wave through fraud. Every one of those outcomes lands inside the FCA Consumer Duty, the Senior Managers regime, and UK GDPR rules on automated decisions. The technology is ready before most governance is.
The Institute of AI works with insurers on exactly this gap. We are an independent professional body with no claims platform to license, so the advice you get is about your risk and your policyholders, not our roadmap.
pillars an insurer is assessed against: Strategy, Governance, Skills, Implementation, Impact
accreditation levels, Student to Fellow, for the people running the model
public Charter, free for any insurer to read and sign
Where AI helps with claims processing.
The strongest claims use cases pair the model's speed with a handler's judgement and an audit trail a reviewer can follow.
First notification of loss triage
Assistants that capture the loss, classify severity and complexity, and route the claim to the right track in minutes, so straightforward cases move and complex ones reach a specialist sooner.
Document and evidence intelligence
Reading police reports, medical notes, invoices, engineer reports, and policy wordings, extracting the facts that matter with every figure traceable back to its source page.
Fraud and anomaly detection
Scoring claims against network, image, and behavioural signals to flag likely fraud for a trained investigator, rather than declining anyone on a model score alone.
Damage assessment from imagery
Estimating motor and property damage from photographs to support faster, more consistent repair-or-replace decisions, checked by an assessor before it drives a payment.
Reserving and leakage control
Surfacing claims that are under or over-reserved and spotting settlement leakage patterns, giving handlers a prompt to review rather than an instruction to act.
Settlement and subrogation support
Drafting decision letters, calculating indemnity, and identifying recovery and subrogation opportunities, with a handler owning and signing off every outcome that reaches the customer.
What must be governed.
Claims AI touches money, health data, and vulnerable people at the worst moment of their year. These are the questions we settle before a model goes live.
Validate the model before it touches a claim
A claims model is a decision-making system, not a convenience feature. It needs an inventory entry, an owner, independent validation, and documented performance across claim types and channels before it goes anywhere near a live file. Assisted decisions are only as trustworthy as the last time someone checked the model still works.
of claims models inventoried and independently validated first
Test that the model treats policyholders fairly
Claims data carries the history of who claimed, who was investigated, and who was paid. Left unchecked, a model can learn to treat postcodes, ages, or demographics as proxies for risk. Fairness testing across protected and vulnerable groups belongs in the build, not the post-mortem, and it maps straight onto the Consumer Duty outcome on fair value.
tolerance for unexplained disparity across protected groups
A decline must survive being explained
If a claim is refused or reduced, the policyholder is entitled to understand why, and the Financial Ombudsman may later ask the same question. A model that cannot support a plain-English reason for its output does not get to make or heavily influence a customer-facing claims decision. Explainability is a gate, not a nice-to-have.
plain-English reason available for every adverse decision
Automated decisions carry legal duties
Claims files hold some of the most sensitive data an insurer touches, including health and financial information. Where AI drives a decision with significant effect, UK GDPR requires a lawful basis, a Data Protection Impact Assessment, and a genuine route to human review. Settle the lawful basis and the review path before the first live claim, not after a complaint.
DPIA completed before any live deployment
A named person owns the outcome
Under the Senior Managers regime, accountability for a claims outcome cannot be handed to a vendor or a model. Every assisted decision needs a human who can overrule it, and every deployment needs a briefed senior owner who answers for how it behaves. The model advises; a person decides and is answerable.
accountable senior owner named per deployed system
Watch the model in production, for good
Claims patterns shift with weather events, fraud tactics, and economic pressure, so a model that was fair and accurate at launch can drift within months. Live monitoring of accuracy, override rates, and outcome disparities, with a defined trigger to pause the model, is what keeps an assisted claims process honest over time.
monitoring of drift, override rates, and outcome fairness
How the Institute of AI helps.
Advice, engineering, and professional standards from a single independent body, with no claims platform to sell.
AI consultancy
Board-level and operational advice on where AI belongs in your claims function, what to govern, and how to align it to the Consumer Duty, the Senior Managers regime, and UK GDPR. No claims platform to sell, so the recommendation is about your risk.
- Claims AI strategy and prioritisation
- Governance mapped to FCA expectations
- Model risk and fairness frameworks
AI solutions
Where you want the system built, our engineering practice designs claims tooling around your controls and audit trail, then hands it to your team with the documentation and knowledge to run and defend it themselves.
- Triage, document, and fraud-support tooling
- Deployed inside your existing controls
- Full handover to your own people
Organisation accreditation
As the UK’s professional body for AI, IoAI accredits organisations against a clear standard and maintains the UK AI Readiness Charter. It is how you show policyholders, regulators, and your own board that your claims AI is run responsibly.
- Organisation accreditation pathway
- The UK AI Readiness Charter, free to sign
- A recognised, independent standard
Make your claims AI
answerable.
A short call with the Institute of AI. Plain answers on where AI fits your claims function, what to govern, and a clear next step. No claims platform to sell.
AI in financial services
How the Institute of AI supports banks, insurers, and investment firms putting AI to work under UK regulation.
Explore the sectorAI consultancy
Independent, vendor-free advice on AI strategy, governance, and model risk for regulated organisations.
Talk it throughThe Charter
The UK AI Readiness Charter: five pledges that show your organisation is getting ready for AI, responsibly.
Read the Charter