Data Science & Machine Learning
Develop, challenge and govern predictive models that improve insurance decisions without sacrificing calibration, explainability or control.
Two practical starting points
The flagship engagements are designed to resolve a defined modelling decision and leave behind a transparent recommendation, not merely a set of metrics.
Determine whether the incumbent pricing model remains calibrated, discriminating, stable and useful—and whether a more complex challenger would create material value.
Independently assess a GLM or machine-learning model and strengthen the evidence, documentation, controls and monitoring needed for its use.
Applied modelling asset
An applied academic demonstrator using synthetic insurance data to compare traditional actuarial and neural-network approaches across predictive performance, calibration, pure premium, explainability and governance.
Core capabilities
Specialist capabilities can be commissioned individually or combined around a defined insurance decision.
GLMs, GBMs, neural networks and ensemble methods for pricing, risk segmentation and insurance decisions.
Explore predictive modellingData preparation, feature design, analytical pipelines, quality controls and reproducible modelling workflows.
Explore data engineeringInterpretability, segment analysis, fairness assessment and communication of model behaviour and limitations.
Explore explainabilityIndependent validation, documentation, controls, audit trails, monitoring and model-change governance.
Explore model governanceA&A modelling philosophy
Model choice should reflect the decision, evidence, operating environment and governance burden.
Define what the model must support before selecting an algorithm.
Understand the incumbent or simpler model before adding complexity.
Ranking power alone does not establish that predictions are decision-ready.
Aggregate performance can conceal material instability or miscalibration.
Explainability, monitoring, controls and operating capacity are part of fitness for purpose.
Start with the modelling decision
A short initial discussion can clarify whether the priority is development, independent validation, governance or a focused model challenge.
