AI's real edge in reinsurance: better data, not a single tool
There isn't a miracle tool coming for reinsurance. The real gains are happening in quieter places: cleaner data, faster extraction, and tighter feedback loops tied to measurable business outcomes.
Veronica Judice, global head of specialist claims solutions at Aon, puts it plainly: claims are the product. Clients buy the promise of a claim experience that supports cash flow and catastrophic response. AI helps deliver that by giving teams faster visibility into trends, claim strengths, and risk signals - not by replacing underwriters.
Forget the silver bullet - build a system
AI is working because teams are plugging multiple tools into existing workflows: data ingestion, document intelligence, entity resolution, and analytics that surface patterns from messy, unstructured inputs. Think of it as a system of systems that makes underwriting, modeling, and claims decisions more consistent and timely.
The payoff shows up before a loss ever occurs. Better data structure and enrichment speed up risk selection, improve pricing confidence, and reduce friction between underwriting, operations, and claims.
Claims-first thinking
If claims are the product, then every step before a loss must support that outcome. That means capturing usable data at FNOL, standardizing intake across brokers and cedents, and closing the loop so insights from claims improve selection and wording on the next deal.
AI helps correlate signals that used to sit idle - adjuster notes, engineering reports, satellite imagery, and third-party data - into something underwriters and claims leaders can act on.
The bottleneck isn't tech - it's talent
Most carriers already have enough tooling to make progress. What's missing is a cross-functional bench that speaks both insurance and technology. You need people who understand property and casualty mechanics, portfolio dynamics, and exposure management - and who can work with data scientists and engineers without translation debt.
Judice's message is clear: keep empathy high while you digitize. The goal is better judgment, not blind automation.
Transparency beats black boxes
Regulators want traceability, not magic tricks. The right AI implementation improves auditability by standardizing inputs, logging decisions, and documenting model behavior. That's essential for solvency and consumer protection mandates.
If you operate in the U.S., the NAIC's AI principles are a good north star for fairness, accountability, and governance. In Europe, the EU AI Act sets expectations on risk tiers and oversight for high-impact use cases.
What leading insurers are doing now
- Run a data inventory: map sources, owners, contracts, update frequency, and quality issues across underwriting, exposure, and claims.
- Standardize schemas: lock in common IDs for insureds, assets, and events to reduce reconciliation work and leakage.
- Industrialize unstructured data: deploy document AI for submissions, bordereaux, loss runs, and engineering reports; push outputs into structured stores.
- Set human-in-the-loop rules: define when models can act, when they assist, and when they must defer to an underwriter or claims lead.
- Track decision quality: measure hit ratio, loss ratio, severity pick accuracy, cycle time, leakage, and customer effort scores - then retrain with feedback.
- Stand up MLOps: version data and models, monitor drift, and keep a clear audit trail for regulators and clients.
- Invest in skills: build pods that pair underwriters, claims specialists, actuaries, data scientists, and engineers on the same backlog.
Practical talent moves
- Upskill underwriters and claims leaders on data literacy and prompt fluency for day-to-day analysis.
- Hire or develop engineers who know insurance data models, not just generic ML.
- Create rotational roles between underwriting, claims, and analytics to reduce handoffs and speed learning.
- Fund internal playbooks for model validation, fairness testing, and documentation - and make them part of performance goals.
If you're building capability across roles, a structured path of short courses can help teams level up without stalling delivery. See curated options by job function here: AI courses by job.
What won't change
Technology doesn't make the decision - people do. Models surface probabilities and patterns based on the data and the instructions you give them. Your advantage comes from the quality of the data, the clarity of the process, and the caliber of the people running it.
Match your pace of innovation to client expectations. There's no shortcut for that, and the firms that win will be the ones that keep strategy, talent, and data moving together.
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