AI Applications in Actuarial Science
Where ML is genuinely useful in actuarial work, where it's overhyped, and what the black box problem means in practice.
The actuarial profession has always been built on mathematical modelling and statistical rigour. So when machine learning arrived, the question wasn't really "should we use this?" — it was "where does it actually help, and where does it create new problems?"
After a few years of experimenting with this in practice, I have some views.
Where ML genuinely helps
Pricing and underwriting
Traditional GLMs are good, but they rely on assumptions of linearity and independence that don't always hold. Gradient boosting and neural networks can capture non-linear relationships and feature interactions that GLMs miss — particularly useful for complex risks.
I've been experimenting with ensemble approaches that combine GLMs with ML models. The performance improvements are real, especially at the tails of the distribution where traditional models tend to underperform.
Claims and fraud detection
Pattern recognition is where ML shines. NLP on claim descriptions, computer vision for damage assessment from photos, anomaly detection for unusual claim patterns — these are areas where the technology has clear, practical value.
Reserving
Chain ladder methods work, but they struggle when claim patterns change or external factors matter. ML models that incorporate economic indicators, weather data, and other external variables can improve reserve accuracy — though the governance challenges are significant.
Where it's overhyped
The honest answer is: anywhere that interpretability matters, which in insurance is almost everywhere. Regulators need to understand how models work. Actuaries need to be able to explain and defend their outputs. A black box that performs 5% better on a test set but can't be explained to a regulator isn't actually useful.
Tools like SHAP have helped close this gap, but they're not a complete solution. Interpretability is still a genuine constraint on where ML can be deployed in practice.
The data problem
ML models are only as good as the data they're trained on. In actuarial applications, historical data often contains biases that reflect past discrimination rather than actual risk. Building models on that data without careful thought about fairness creates real regulatory and ethical exposure.
This is an area where actuarial training — with its emphasis on understanding the underlying risk and not just the statistical pattern — is genuinely valuable.
My practical advice
Start with well-defined, small problems where you can validate outputs against something you already understand. Don't abandon traditional actuarial methods — use them to sanity-check your ML outputs. Invest heavily in model governance before you invest in model complexity.
The combination of deep domain knowledge and ML skills is rare and genuinely valuable. If you're an actuary curious about this space, the technical skills are learnable. The domain knowledge you already have is the hard part.