Conceptual soundness
Challenge the purpose, methodology, assumptions, feature choices, segmentation, limitations and consistency with the underlying risk process.
Independent evidence for model use
Validate the decision—not only the algorithm.
A technically sophisticated model can still fail through weak data, inappropriate assumptions, implementation differences, unstable performance or use outside its intended scope. Effective validation follows the complete chain from purpose and design through production output and management action.
Arocha & Associates provides independent challenge across actuarial, financial and machine-learning models, combining quantitative testing with practical judgement, documentation review and governance assessment.
The review can cover one material model, a model change or a portfolio-level validation framework, with depth proportionate to risk and intended use.
Core validation dimensions
Test the model from concept through implementation.
Data & implementation
Trace data, transformations and code; reproduce key results; and compare the approved design with the model operating in the production environment.
Performance & use
Test calibration, discrimination, stability, sensitivities and segments, then assess whether controls and human judgement support appropriate decisions.
Validation lenses
Focus testing where model risk can become business risk.
- Data suitability, representativeness, quality and lineage.
- Methodology, assumptions, parameterisation and expert judgement.
- Calibration, discrimination, error, sensitivity and uncertainty.
- Stability, drift, extrapolation and performance across material segments.
- Explainability, fairness considerations, limitations and appropriate use.
- Documentation, approval, change control, monitoring and escalation.
Independent validation cycle
Reconstruct the evidence before reaching a conclusion.
Findings are linked to materiality and use, enabling management to distinguish critical remediation from desirable enhancement.
- 01
Scope
Define the decision, model boundaries, materiality, evidence and validation criteria.
- 02
Reconstruct
Trace methodology, data, code, calculations, controls and production implementation.
- 03
Test
Perform quantitative, qualitative, sensitivity and use-based challenge.
- 04
Conclude
Rate findings, define remediation and establish monitoring and acceptance conditions.
Typical outcomes
A defensible view of fitness for purpose.
An independent validation report with prioritised, evidence-based findings.
Clear conclusions on model strengths, limitations and conditions of use.
A proportionate remediation plan with accountable owners and timelines.
Stronger monitoring, documentation and model-risk governance.
Start with the challenge question
What would an independent reviewer challenge first?
We can scope a focused review, test the evidence and provide a clear conclusion on whether the model is ready for its intended use.