Insurance

Business problem

• Underwriters decline or ignore submissions they cannot process fast enough
• The best risks bind with whoever quotes first

AI solution

• LLMs ingest broker emails and attachments, extract and enrich risk data
• Score against appetite and win-propensity; route prioritized, pre-populated cases

Business value

• Revenue growth — quote more, faster, and the right risks

Business problem

• Roof condition, vegetation and wildfire/hail exposure unknown without a $50–150 inspection

AI solution

• Computer vision on aerial, satellite and street imagery scores roof condition, defensible space and peril risk per property
• At quote, renewal and portfolio review

Business value

• Risk reduction — inspections replaced, deteriorated roofs caught pre-bind
• Regulator-approved for rating

Business problem

• Expertise is scarce and retiring
• Underwriters spend 40–60% of time on synthesis, lookup and drafting

AI solution

• GenAI summarizes risk files, answers guideline questions with citations (RAG over manuals)
• Drafts referral memos and quote rationale; compares terms

Business value

• Operational efficiency — 10–20% productivity gains expected
• Expertise attrition mitigated

Business problem

• Proxy rating variables misprice individual driving risk
• No lever to reduce accident frequency

AI solution

• Time-series ML on telematics scores mileage, braking, speeding and phone distraction
• Prices behaviourally, coaches drivers, detects crashes for automatic FNOL

Business value

• Revenue growth — proven risk segmentation and retention economics

Business problem

• Enormous routine volumes — billing, ID cards, coverage, claims status — at $5–15 per contact
• Queues damage customer experience

AI solution

• LLM assistants (chat and voice) authenticate customers and resolve routine journeys end-to-end
• Hand-off of complex cases with full context

Business value

• Customer experience — 24/7 service
• Lower cost to serve

Business problem

• Claims assigned round-robin; complex, litigation-prone claims recognized late
• The largest driver of leakage, cycle time and complaints

AI solution

• ML scores every claim at FNOL for severity, complexity, fraud/litigation risk and STP eligibility
• Routes to the right track — instant pay, desk, specialist, nurse, SIU

Business value

• Cost reduction — leakage cut by several loss-ratio points
• Faster cycle time, fewer complaints

Business problem

• Physical inspection adds days and $100+ per auto claim
• Supplements on 60%+ of estimates create rework and rental cost

AI solution

• Computer vision on policyholder photos identifies damaged parts, repair vs. replace and the estimate in minutes
• Touchless settlement and instant total-loss identification

Business value

• Operational efficiency — days to minutes at massive volume

Business problem

• Adjusters spend hours per file on medical records, legal correspondence and years of notes

AI solution

• GenAI summarizes claim files, extracts medical chronologies, drafts correspondence
• Paired with predictive next-best-action guidance

Business value

• Operational efficiency — double-digit adjuster capacity gains
• Faster, more consistent decisions

Business problem

• ~10% of P&C claims show fraud indicators; manual referrals catch a fraction
• False positives slow honest claims

AI solution

• Every claim scored in real time by supervised models, anomaly detection and GenAI analysis of unstructured content
• Explainable alerts with evidence routed to SIU

Business value

• Risk reduction — direct loss-cost reduction
• Faster payment of legitimate claims

Business problem

• Submissions, loss runs, ACORD forms, medical records and police reports arrive as unstructured PDFs and emails
• Staff read and rekey — days of latency and error

AI solution

• OCR, layout models and LLMs classify documents, extract and validate fields
• Clean data pushed into core systems; LLMs handle high-variety formats

Business value

• Operational efficiency — the foundational enabler every other use case consumes