AI surge strains insurers as governance and audit demands rise
AI has moved from side project to front-office standard. About 70% of buy-side firms, including insurers, now use AI in investment operations-up from roughly 10% a year ago, according to SimCorp's 2026 InvestOps Report.
Use is concentrated in decision support and portfolio management. The promise: faster analysis, fewer manual tasks, and more focus for investment teams.
What insurers are prioritising right now
- Consolidating tech vendors and platforms (58%).
- Modernising architecture and data infrastructure (54%).
- Choosing vendors for stability first (57%), then access to innovation (54%) and analytical flexibility (47%).
- Strengthening governance: 40% want clear frameworks; 35% need auditable, compliant outputs.
Translation: scale is useless without controls. If your audit trail is weak, your AI program becomes a liability-especially under tightening regulatory expectations.
Why this matters for insurance portfolios
Large books, multi-asset exposures, and liquidity constraints create operational drag. AI helps with data quality, signal generation, and workflow speed-if you can prove it works, monitor it, and show your homework.
That means clean inputs, documented models, and a chain of accountability from PM to platform to policy.
Immediate actions for insurance investment teams
- Define approved front-office use cases with success metrics (hit rate, tracking error impact, time saved).
- Stand up a governance framework: policy, accountable owners, model inventory, risk tiers, review cadence.
- Enforce full auditability: data lineage, prompts/config history, versioning, decision logs, overrides.
- Vet vendors on durability: financial strength, roadmap, security posture, and exit options.
- Consolidate overlapping tools to reduce integration debt and reconciliation breaks.
- Fix the data layer first: golden sources, entitlements, quality checks, and reference data alignment.
- Train your people; pair AI rollout with change management and clear escalation paths.
Controls regulators expect (build these in)
- Explainability for model-driven decisions and overrides.
- Bias and performance monitoring with thresholds and alerts.
- Stress testing and backtesting against historical regimes.
- Human-in-the-loop for high-impact decisions.
- Third-party risk due diligence (security, resilience, data residency, certifications).
- Incident reporting procedures and kill switches.
- Protection against use of PII and MNPI in training or prompts.
Alternatives are the next big lift
Firms see the most tech-driven upside in private markets and alternatives: 51% in 2026 vs 27% in 2025. Expect focus on document intake, deal screening, cash flow forecasting, and workflow automation.
Warning: data is sparse and messy. Your controls need to be tighter-otherwise small errors compound into big valuation and liquidity mistakes.
Questions to put to every AI vendor
- Show us audit logs end-to-end (inputs, versions, outputs). Exportable?
- What's the model update cadence and how are changes validated?
- Data location, retention, and segregation-prove it.
- Evidence of stability (financials, client churn, roadmap, support SLAs).
- Interoperability with our PMS/OMS/IBOR and data lake-native or custom?
- Controls for PII/MNPI, and prompt/data filtering.
- Failure modes and fallbacks-what happens when confidence is low?
- Total cost of ownership over 3 years, including integration and change.
Metrics that show real value
- Time-to-decision and straight-through-processing rates.
- Reduction in reconciliation breaks and manual touchpoints.
- Backtest error and stability across regimes.
- Model exception rate and override quality.
- Capacity uplift per PM/analyst without added risk.
Where to go from here
Treat AI like any other material model: governed, measured, and explainable. Start small, prove value, then scale on a simplified platform stack.
For context on the market shift, see SimCorp's report overview here. For teams upskilling on practical tooling in finance, explore curated resources here.
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