Model interpretability
Use global and local techniques—including feature importance, partial dependence and attribution—to understand patterns, individual predictions and limitations.
Clarity before consequence
Explain the model in the context of its use.
Explainability is not a chart added after model development. A useful explanation must clarify what drives a prediction, where the model may be unreliable, how its behaviour varies across segments and what a decision-maker should do with the output.
Arocha & Associates combines actuarial judgement, statistical interpretation and governance design to make complex models more transparent to developers, validators, management and oversight functions.
Support can focus on one model or decision, or establish a proportionate explainability framework across a wider model portfolio.
Core support
Move from technical interpretation to accountable use.
Fairness & behaviour analysis
Examine performance and outcomes across relevant groups, test proxies and sensitivities, and distinguish statistical differences from decision concerns.
Documentation & governance
Define the explanation evidence, limitations, approvals, escalation and ongoing monitoring required for the intended use and level of model risk.
Different audiences, different questions
Build explanations that help each stakeholder act.
Developers
Which features, interactions, edge cases and data limitations shape model behaviour?
Owners & management
When should the model influence a decision, and when is human judgement required?
Validators & oversight
Is the explanation faithful, reproducible and sufficient to support challenge and approval?
A decision-centred review
Test whether the explanation is useful—not merely available.
The work links model behaviour with the decision context, affected stakeholders, evidence requirements and governance response.
- 01
Context
Define the model use, audience, materiality and explanation questions.
- 02
Diagnose
Analyse drivers, interactions, segments, errors, stability and limitations.
- 03
Explain
Create faithful narratives and visual evidence suited to each audience.
- 04
Govern
Document use conditions, human oversight, monitoring and escalation.
Typical outcomes
More confidence in how the model is understood and used.
Model-level and prediction-level explanations linked to the business decision.
Clear evidence on segment behaviour, fairness considerations and limitations.
Audience-specific documentation for owners, management and validators.
Defined human oversight, monitoring indicators and escalation triggers.
Start with the explanation gap
Which model decision is hardest to explain today?
We can assess the current explanation evidence, identify the material gaps and design a proportionate approach to interpretation and oversight.