ActuariesvsInsurance Underwriters

until most of the change has landed

Medium-High adoption

until most of the change has landed

High adoption

AI is speeding up actuarial modelling, data preparation and report drafting, while pricing, reserving and sign-off stay under professional control.

AI significantly automating risk assessment and decision-making.

1. Machine-learning pricing platforms
2. Code assistants
3. Automated data pipelines
4. Generative drafting and summarisation
5. Regulatory expectations on model risk
6. Low observed usage so far

1. Availability of Vast Customer & Risk Data
2. Advancements in Machine Learning & Predictive Analytics
3. Demand for Faster Quoting & Policy Issuance
4. Competitive Pressures in the Insurance Market
5. Rise of Insurtech & AI-Native Competitors
6. Need for More Granular & Personalized Risk Pricing
7. Increased Computational Power
8. Improved Fraud Detection Capabilities
9. Regulatory Push for Data-Driven Decision Making (with caveats)
10. Desire to Reduce Manual Underwriting Costs

1. Machine-learning model validation
2. Programming with assistants
3. Model governance and explainability
4. Communication with non-specialists
5. Data engineering awareness
6. Professional judgement in assumption setting

1. Data Analysis & Interpretation
2. AI Model Understanding & Validation
3. Risk Assessment & Critical Judgment (for complex cases)
4. Communication & Negotiation Skills
5. Business Acumen & Industry Knowledge
6. Ethical AI & Regulatory Awareness
7. Adaptability & Continuous Learning
8. Portfolio Management & Strategic Thinking

1. Microsoft 365 Copilot
2. GitHub Copilot
3. Akur8
4. Databricks
5. ChatGPT

1. Zesty.ai (for property risk)
2. Shift Technology (for fraud detection & claims automation)
3. Cytora (AI-powered commercial underwriting)
4. Guidewire Underwriting Management / Duck Creek Policy
5. Internal Proprietary AI Models