Will Gen AI caution worsen Singapore's insurance talent crunch?
Singapore's insurers are under strain from global uncertainty, tighter regulation, and the push into generative AI. Hiring remains strong across underwriting, actuarial, claims and compliance - yet the supply of specialist talent isn't keeping up.
Digitalisation across finance is also fuelling demand for cyber risk coverage. Employers now want people who blend technical skill with regulatory fluency to price, structure and manage policies for digital assets and liabilities.
Where demand is heating up
In underwriting, expectations are shifting from routine tasks to higher-value judgment. Teams are being asked to run root-cause analysis on complaints, build and interpret advanced models, and adjust standards and terms to fit internal guidelines and evolving customer needs.
Market insight matters more. Underwriters are expected to track competitor positioning, improve fraud risk controls, tighten regulatory compliance, and keep a firm eye on profitability.
Actuaries are in demand for pricing strategy, financial modelling and business performance. The standout skill: turning complex analysis into clear recommendations and risk metrics that senior leaders can act on. Salary trends reflect the competition for these capabilities.
Cyber coverage is rising with the wider digital push. Insurers need talent that understands system security threats, compliance gaps and legal liability - and can translate that into practical pricing and coverage structures.
Why caution on Gen AI is stretching teams
Most firms see the upside of generative AI, but many are rolling it out slowly. That caution is reasonable - and it raises the bar for governance, documentation and skills. Without clear talent pathways, adoption stalls and workloads fall back on already thin teams.
Regulatory expectations are increasing, especially around fair and accountable AI use. The industry needs people who can link model choices to controls, explain outcomes, and prove compliance. For reference, see MAS guidance on responsible AI and the FEAT principles here.
At the same time, retention is under pressure. A perceived lack of growth is still a top reason people leave. Clear progression and structured development are now a competitive advantage.
Practical steps to close the gap
- Define accountability. Map responsibilities across the three lines of defense for AI-enabled processes. Set decision rights for underwriting changes, model updates and exception handling.
- Build a skills spine. Cross-train underwriters and actuaries in model risk, prompt discipline, scenario testing and documentation. Pair training with live use cases and feedback loops. If you need structured options, explore role-based AI paths here.
- Re-scope roles for higher-value work. Underwriting: portfolio analytics, claims feedback integration, fraud indicators. Actuarial: pricing squads embedded with product, clear translation to business metrics. Claims: automation oversight and leakage control. Compliance: early involvement on data use and explainability.
- Start with contained use cases. Document summarisation, policy wording quality checks, FNOL triage, straight-through processing triggers with human review. Define measurable outcomes (loss ratio impact, TAT, complaint rate) and stick to them.
- Tighten guardrails. Control PII exposure, set vendor risk criteria, keep model cards and audit trails, and require human-in-the-loop on decisions that affect coverage or pricing.
- Upgrade hiring and assessment. Use case-based tests for judgment and communication, not just technical depth. Consider mid-career conversions for analytics-minded candidates and local partnerships for early talent pipelines.
- Make growth visible. Offer dual tracks (management and specialist), formal rotations, and recognition for technical leadership. Publish clear criteria for progression and reward it.
What good looks like in 6-12 months
- Two to three AI use cases in production with documented controls and business outcomes
- Shorter underwriting cycle times and better hit ratios without compromising risk appetite
- Actuarial recommendations tied to simple risk metrics understood by non-technical stakeholders
- Retention improving in underwriting and actuarial teams due to visible progression and learning
- Audit-ready logs and model documentation that satisfy internal and external reviews
The bottom line
Caution on Gen AI doesn't have to deepen the talent crunch. With the right governance, role design and training, you reduce risk while moving faster on high-value work. The firms that set clear pathways now will hire better, keep their best people, and outperform peers who wait.
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