Oman Re Roundtable: AI's Impact on the Future of Insurance

Oman Re: Roundtable Discussion on the Future of the Insurance Sector Driven by AI

AI is moving from pilot projects to line-of-business impact. For insurers and reinsurers, the upside is clear: better risk selection, faster claims, tighter expense ratios, and sharper portfolio steering.

This roundtable centers on one question: how do we turn AI from a cost center into measurable underwriting and claims gains without adding model risk or compliance headaches?

High-Impact Use Cases to Prioritize

• Underwriting triage and decision support: Pre-bind risk scoring, broker submission ranking, and appetite checks on loss runs and bordereaux.
• Pricing enhancement: Gradient-boosted models to sharpen segmentation; GLM-plus frameworks for explainability and rate filings.
• Claims automation: FNOL extraction from documents, fraud propensity scores, subrogation likelihood, and straight-through processing for low-severity claims.
• Reserving and capital: Early signal detection on frequency/severity shifts; scenario stress for inflation and supply-chain shocks.
• Reinsurance optimization: Ceded strategy simulations, treaty wordings analysis with large language models, and portfolio-level attachment optimization.

Data Foundation That Actually Ships

• Data contracts and lineage: Define ownership, SLAs, and versioning for core tables (policies, claims, exposures, bordereaux, loss runs).
• Quality gates: Automated checks for leakage (e.g., missing peril, geocode drift, coverage mismatches).
• Unstructured data: OCR and text parsing for submissions, invoices, medical reports, and adjuster notes; label once, reuse everywhere.
• Privacy and residency: Clear patterns for PII handling, UAE/Oman/GCC data transfer rules, and redaction pipelines.

Model Choices That Fit Insurance

• Predictive models: GLMs for transparency; GBMs and shallow nets for lift where filings permit; monotonic constraints where business sense matters.
• Generative AI in operations: Retrieval-augmented generation for guidelines, policy wordings, and regulatory Q&A; prompt libraries with audit trails.
• Explainability: Use SHAP or permutation importance; store rationale artifacts with every model run for audit and file-and-use needs.
• Model risk management: Version the dataset, code, parameters, and outputs; implement challenger models and backtesting as standard.

Governance and Compliance

Use a simple, repeatable framework that satisfies both internal audit and supervisors. Anchor to recognized guidance and make auditability a daily habit, not a once-a-year sprint.

• Policy: Define acceptable use, prohibited features, and human-in-the-loop checkpoints by process (underwriting, pricing, claims).
• Risk controls: Bias checks by segment, stability testing across time, and red-team prompts for generative systems.
• Records: Log datasets, prompts, responses, approvals, and overrides. Keep explanations tied to decisions.
• Reference frameworks: NIST AI Risk Management Framework (NIST AI RMF) and EIOPA's AI governance principles (EIOPA AI Governance).

ROI: Metrics That Matter

• Underwriting: Quote-to-bind +3-7 points, time-to-quote down 30-60%, hit ratio lift on target segments, loss ratio improvement 1-3 points in year one.
• Claims: Cycle time down 25-50%, LAE reduction 10-20%, leakage down 15-30%, fraud detection lift measured by confirmed saves.
• Portfolio: Exposure steer to risk-adjusted return targets; ceded cost per point of PML reduction; cat volatility buffers validated by scenarios.

People and Operating Model

• Cross-functional squads: Underwriter, claims lead, actuary, data scientist, engineer, and compliance working from a shared backlog.
• Product ownership: Business PO with authority on acceptance criteria and rollout; tech lead owns reliability and security.
• Change management: Clear underwriting and claims playbooks, incentive alignment, and "shadow mode" before full enablement.
• Upskilling: Practical training for prompts, validation, and exception handling; measure adoption, not attendance.

Reinsurance Lens for Oman Re and Partners

• Submission analytics: Normalize cedant data, score treaty attractiveness, and surface exclusions or clash risk early.
• Cat and exposure: Blend model output with market signals; detect drift in event footprints and secondary peril exposures.
• Wordings review: Use LLMs to flag ambiguous clauses, exclusions coverage creep, and in-consistent definitions across treaties.
• Portfolio steering: Optimize attachments, reinstatements, and aggregate covers against capital and earnings targets.

90/180/365-Day Execution Plan

• Day 0-90: Pick two use cases (e.g., underwriting triage, claims FNOL). Lock data contracts, define KPIs, ship pilot in shadow mode.
• Day 90-180: Scale to production with human oversight. Add monitoring, explainability, and compliance checks. Start benefit tracking.
• Day 180-365: Expand to pricing support and reinsurance optimization. Introduce challenger models and portfolio-level steering.

Roundtable Questions Worth Asking

• Which decisions, if improved by 10%, would change our combined ratio the most?
• Where does data quality block us today and who owns the fix?
• What human approval is mandatory at each step, and how is it logged?
• Which KPIs prove lift within 60-90 days, not just in theory?
• How do we keep models stable through seasonality, inflation waves, and new perils?

Next Steps

If your team needs a fast, practical upskill path, explore role-based programs and certifications that map to underwriting, claims, and data roles.

• AI courses by job role
• Popular AI certifications

AI wins are earned by focus, clean data, and governance you can explain in a board meeting. Keep it small, measurable, and repeatable-and scale what works.