Auto-Validation Rules
This article explains how auto validation rules work in Model Reef input fields.
You will learn:
What types of validation the system performs automatically.
How bounds, types and patterns contribute to validation.
How to respond to validation warnings.
Auto validation is designed to catch obvious issues early without blocking legitimate but unusual scenarios.
1. Types of auto validation
Model Reef applies several kinds of checks when you enter or change values:
Type checks
Numeric fields must contain numeric values.
Percent fields expect values in an appropriate range.
Bounds checks
Values are compared against any configured minimum and maximum bounds.
Pattern checks
Large jumps or sign flips compared to recent periods may be highlighted.
Values very different from historical actuals may be flagged.
These checks are lightweight and run as you edit.
2. How validation feedback is shown
When validation detects a potential issue, the interface may:
Highlight the cell or field in a warning colour.
Display a tooltip or message explaining the issue.
Show an icon or marker in the margin of a panel.
Validation messages are advisory; they do not by themselves change data.
3. Interaction with bounds and units
Auto validation rules use:
Bounds, if defined, as thresholds for warnings.
Units and display scale to ensure that checks are applied in the correct context.
This is why setting realistic bounds and consistent units improves the usefulness of validation feedback.
4. Structural validation
Beyond numeric values, some structural changes trigger validation, for example:
Changing variable type from one category to another.
Moving variables between branches.
Reclassifying Data Library entries.
In these cases, the system may request confirmation and explain the implications for financial statements.
Dealing with validation warnings
Warnings are there to support judgement, not to replace it.
6. Validation and collaboration
Validation is especially helpful in collaborative models:
It reduces the chance that one collaborator introduces extreme or inconsistent values unnoticed.
It gives Owners and senior Editors more confidence in the integrity of inputs.
Combined with notes, tags and attachments, it forms part of the model's governance toolkit.
Auto validation plus human review produces the best results.
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