AI you can put in front of a regulator.

Motor & property first-notice-of-loss classification, severity scoring, and routing.

Inputs:

FNOL free-text, telematics summary, policy metadata, prior-claim features.

Outputs:

Severity band (1–5), fast-track eligibility, suggested handler pod, confidence score.

Human-in-the-loop:

Every decline / fast-track override reviewed by a licensed adjuster before customer notification. Confidence < 0.75 auto-routes to human.

Primary metric:

Balanced accuracy vs. adjuster gold-standard; leakage-cost delta.

Fairness testing:

Demographic parity + equalised odds tested across postcode-derived income deciles and age bands. Quarterly re-test.

Refresh cadence: Quarterly retrain; drift-triggered off-cycle.

Commercial & specialty submission ingestion — SOV, loss runs, broker email threads.

Inputs:

PDF/XLSX submissions, broker email bodies, prior-year benchmark data.

Outputs:

Structured risk record, exposure summary, referral flags, missing-info list.

Human-in-the-loop:

Underwriter is the decision-maker. Model outputs are proposals with source-document citations; no auto-bind.

Primary metric:

Field-level extraction F1 vs. UW-verified ground truth; cycle-time to quote.

Fairness testing:

Not a rating model. Monitored for systematic under-extraction on non-English submissions and small-broker templates.

Refresh cadence: Monthly eval on rolling sample; retrain on template drift.

Suspicious-claim detection for motor bodily injury and property.

Inputs:

Claim narrative, network features, prior-claim history, external watchlists (contracted).

Outputs:

SIU-referral score with top contributing signals (SHAP-style explanations).

Human-in-the-loop:

Model never denies a claim. Scores above threshold create an SIU referral; investigator makes the call.

Primary metric:

Precision @ investigator-capacity; false-positive rate by cohort.

Fairness testing:

Adverse-impact ratio tested across protected proxies; disparate-impact review before each release.

Refresh cadence: Quarterly retrain with SIU feedback loop.

Inbound policyholder servicing — status checks, endorsements intake, callback booking.

Inputs:

Live audio, policy context, prior interaction history.

Outputs:

Task completion, transcript, sentiment, escalation flag.

Human-in-the-loop:

Warm-transfer to human on complaint keywords, sentiment threshold, vulnerable-customer signals, or any coverage decision.

Primary metric:

First-contact resolution; escalation appropriateness; WER by accent cohort.

Fairness testing:

WER parity tested across accent, gender and age cohorts; vulnerable-customer routing audited monthly.

Refresh cadence: Monthly prompt & tool eval; ASR model quarterly.

Actuarial support — reserve triangulation, portfolio drift detection.

Inputs:

Aggregated triangles, exposure, calendar-year features.

Outputs:

Reserve range, driver decomposition, outlier flags.

Human-in-the-loop:

Chief Actuary owns the sign-off. Model is a decision-support tool, not a booking system.

Primary metric:

Back-tested reserve error vs. ultimate; stability of driver decomposition.

Fairness testing:

N/A — no individual-level decisions.

Refresh cadence: Quarterly, aligned to reserving cycle.

Internal assistant for producers — product Q&A, appetite lookup, quote assembly.

Inputs:

Broker query, appetite guides, rate books (retrieval-grounded).

Outputs:

Grounded answer with citations to source documents.

Human-in-the-loop:

Producer is the user; every answer shows sources. No customer-facing outputs without producer review.

Primary metric:

Groundedness (citation-supported claim rate), producer thumbs-up rate.

Fairness testing:

Refusal-rate parity across product lines; hallucination audit weekly on held-out queries.

Refresh cadence: Continuous retrieval refresh; model prompt eval weekly.