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Aipedia: Insurance brokers
AI can help brokers analyse claims ratios, commission and customer outcomes for fair value work, but the conclusions are only as reliable as the data and model behind them. A flawed analysis can leave poor value undetected and expose the firm to regulatory action.
Part of: AI in insurance brokers
In short
Brokers who distribute retail insurance must assess whether products and their own remuneration offer fair value, under the FCA's product governance rules and the Consumer Duty price and value outcome. AI tools now crunch claims, complaints, cancellation and commission data and draft the assessment. Data quality is the weak point: missing claims data, mismatched product codes or a model that averages away a poor-value segment. Regulatory exposure is mainly with the FCA, and fines are often uninsurable. Broker E&O cover is more likely to be relevant if customer redress claims follow.
A fair value assessment draws on large data sets: premiums, commissions and fees, claims frequency and acceptance, complaints, cancellations and the characteristics of customers who buy. Brokers increasingly use analytics and AI tools to clean this data, segment customers, spot outliers and draft the written assessment and board report. Insurers supply some of the data; the broker supplies its own remuneration and service data.
AI is well suited to finding patterns in large data sets. It is less suited to judging whether a pattern is a data error or a real customer problem.
There is also a temptation to let the tool write the conclusion as well as the analysis. Generative AI will produce fluent text describing a product as offering fair value whether or not the data supports it, so the narrative must be checked against the numbers. Where a broker relies on data or a fair value assessment provided by the insurer as manufacturer, it should still test whether its own remuneration and service change the value the customer receives.
Boards must approve the annual Consumer Duty board report, so directors need to understand where AI analysis fed into it.
Illustrative: a broker uses an AI tool to analyse its add-on products. The tool reports a healthy claims acceptance rate across the book. It does not detect that claims for one add-on sold through a single online channel are recorded under a different product code, so that add-on's very low claims ratio is masked. The board approves the assessment. A later FCA review finds the add-on offered poor value and asks the firm to consider redress for affected customers.
Other risks include a model trained on insurer data that does not include declined claims, and a written assessment that overstates the analysis performed.
Most fair value failures are regulatory matters rather than negligence claims by a single client. FCA fines and penalties are often uninsurable or excluded from broker PI. Customer redress schemes and resulting complaints can be more complex: whether a broker's E&O policy responds to redress depends heavily on the wording and the circumstances. Broker PI meeting MIPRU 3.2.4R must cover Ombudsman awards, which may be relevant if individual complaints follow. See fair value assessments and PI for more.
See also AI-automated renewals, AI pricing under delegated authority, Consumer Duty for brokers and the insurance broking AI hub.
Apex arranges PI for insurance brokers. A broker can check how your E&O policy treats regulatory investigations, redress and Ombudsman awards, which matters most when fair value is in question.
AI can help analyse data and draft the document, but the firm remains responsible for the conclusions. A person with product knowledge should check the data, review the findings and make sure the assessment accurately describes what was done.
Common issues are inconsistent product codes, missing data on declined or withdrawn claims, remuneration data held separately from claims data, and small segments that disappear in book-wide averages. Each can hide poor value.
Fines and penalties are often uninsurable or excluded. Redress and complaints are a separate question that depends on the wording, so check how your policy treats regulatory redress and Ombudsman awards.
The FCA's October 2026 good practice page expects AI responsibilities to be mapped to senior management functions and ownership assigned by use case. For fair value, that is often the SMF responsible for Consumer Duty or product governance.
Tell us about your firm and the work you do. A broker will review how your current PI treats AI-assisted work, or arrange new cover. No fee for a quote. Or call 0117 325 0027.
Start a PI quote Request a callbackApex Insurance Brokers Limited is authorised and regulated by the Financial Conduct Authority. Registered in England and Wales, company number 07014570. This page is general information about how AI is used in the profession and how insurance may respond. It is not legal, regulatory or insurance advice on your circumstances, and it does not guarantee that cover will be available or on what terms.
