How are insurance brokers using AI?
Brokers can test AI on document comparison, renewal preparation and claims intake. Each needs checks for source accuracy, client-data handling and a named broker or handler who decides what goes to the client.
Book an AI Risk & Efficiency Audit
Choose a candidate workflow and examine its documents, data permissions and review steps. The examples below are tasks to evaluate, not evidence that brokers generally use AI this way or achieve a particular saving.
Workflow examples to evaluate
Compare document-handling tasks, broker decision support and informal staff use separately. Each needs its own approval boundary.
1. Document-handling tasks
For a first test, select a document task with known inputs and a result a broker or handler can check against the source.
- Policy Checking & Comparison: Test whether the proposed tool can flag differences between a slip and final policy wording, including exclusions, limits and definitions. Have a broker check the source clauses, missed differences and incorrect flags before relying on the comparison.
- Claims Triage & FNOL: For First Notice of Loss (FNOL), test extraction of date, cause and estimated loss from approved email or form samples. Review missing and ambiguous fields before any claims-system write; define who can approve or correct the record.
- Submission Prioritisation: Evaluate extraction of industry, turnover and location against an agreed risk-appetite framework. Check how incomplete submissions and uncertain scores are handled, and keep the quoting-priority decision with the authorised broker.
2. Broker decision support
For decision-support trials, record what the tool may suggest and what a broker must independently check before giving advice.
- Renewal Intelligence: Test whether the tool can identify renewal questions from the client file and approved market evidence. Require source references for any proposed cyber-claims trend, inflation adjustment or sum-insured concern. The broker should check those sources and the client's circumstances before discussing a coverage change.
- Market Matching: If historical placement data is used to suggest markets, check its date, permitted use and relevance to the current risk. Verify current underwriter appetite before making a placement decision; historical matches should not be treated as current confirmation.
3. Informal staff use
Include unapproved staff use in discovery. Ask whether surveyor reports, underwriter emails or spreadsheets are being put into tools outside the approved workflow. Record the exact product, account and data involved rather than assuming that a named brand or a public interface determines its terms.
Where the firm cannot establish the data permissions, provider terms or review owner, hold that client-data use for assessment. Give staff an approval and escalation route for proposed tools.
Responsibilities to review before client use
Ask the firm's legal or compliance owner to review the proposed use against applicable duties, client commitments and Terms of Business. Do not infer a legal outcome from the use of an AI tool alone.
Check policy summaries: require a reviewer to compare a proposed client summary with the policy wording and source references. Escalate an unsupported coverage statement before sending it. Liability and insurance consequences need case-specific professional assessment; this example does not determine them.
Check the input data: inspect inconsistent names, missing fields and duplicate records in the chosen workflow. Budget the data preparation, review, integration and monitoring work for that test rather than using an unsupported fixed spending split.
Choose a bounded trial
Compare a bounded trial with the current workflow: source accuracy, missed fields, reviewer effort, client-data handling and unauthorised writes. Use the results to decide whether to stop, change or expand it.
Record the permitted task, data boundary, review owner and escalation conditions before the test starts. Keep any unresolved client, supplier or professional-duty question visible rather than treating the trial as clearance.
Are your brokers using AI tools you don't know about?
Gain visibility into your firm's actual AI usage and data risks with our AI Risk & Efficiency Audit. We identify where your data is going and how to bring it back under control.
Explore Managed AI Governance & Support
See Implementation Projects
Need a controlled AI route for a regulated team?
For a broker trial, review policy comparison, claims-intake writes or renewal suggestions alongside the data permissions and human checks they need.
