Model strategy & challenge
Define the target decision, success criteria and constraints, then assess whether the incumbent approach remains calibrated, stable and useful.
Model choice follows evidence
Use the simplest model that earns the right to decide.
Greater complexity does not automatically create greater value. The appropriate model depends on the business question, data, performance evidence, operational constraints and the level of explanation and governance the decision requires.
Arocha & Associates combines traditional actuarial methods with modern machine learning to develop and challenge models for pricing, underwriting, claims, risk segmentation and other insurance applications.
Engagements may start with an incumbent-model diagnostic, a challenger comparison or the development of a controlled model from data through implementation.
Core support
Connect modelling technique with decision value.
Development & comparison
Develop and compare GLMs, gradient-boosting models, neural networks or hybrid approaches using consistent data, metrics and validation evidence.
Integration & monitoring
Translate model output into an operating decision with documentation, human oversight, implementation controls, performance monitoring and recalibration.
Evidence before selection
Compare what matters for the intended use.
- Calibration in aggregate and across material business segments.
- Discrimination, lift and error measures appropriate to the target.
- Stability over time, drift, extrapolation and sensitivity to assumptions.
- Explainability, operational complexity and ability to support human judgement.
- Implementation effort, monitoring requirements and governance burden.
Applied example: explore the interactive GLM–FFNN model comparison demonstrator.
A decision-led modelling cycle
Establish a credible baseline before adding complexity.
The work separates genuine model improvement from apparent gains caused by data, validation design or inconsistent comparison.
- 01
Question
Define the decision, target, data, constraints and business success measures.
- 02
Baseline
Build or diagnose a transparent benchmark and verify data and evaluation design.
- 03
Challenge
Compare alternatives across performance, stability, explanation and operating cost.
- 04
Decide
Select, document, implement and monitor the model appropriate for the use.
Typical outcomes
A model recommendation that management can act upon.
A documented incumbent-model diagnostic and evidence-based challenger comparison.
Transparent performance, calibration and segment-level evaluation.
A clear model-choice recommendation with trade-offs and limitations.
Implementation, monitoring and governance requirements for controlled use.
Start with the modelling decision
Would a more complex model materially improve the decision?
We can diagnose the incumbent model, build a credible challenger and translate the evidence into a practical recommendation.