ETQ014
Separate model complexity from decision communication by mandating interpretable output layers so auditors can explain and challenge every analytical conclusion.
CyberTRIZ analysis · Audit contradiction ETQ014 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Analytical Precision vs Interpretability
Business ContextSophisticated statistical models, machine-learning techniques, and advanced analytics can improve anomaly detection and risk classification. Increasing analytical complexity, however, can make outputs difficult for auditors, management, reviewers, and governance bodies to understand or challenge.
Audit TRIZ ResolutionSeparate analytical complexity from decision communication. Advanced models can perform detection where they provide demonstrable value, while interpretable indicators, validation procedures, supporting evidence, and simplified decision layers explain why identified conditions require audit attention.
Applicable TRIZ Principles
Principle 1 – Segmentation separates complex analytical processing from the explanation used for audit decisions.
Principle 24 – Intermediary introduces interpretable outputs between sophisticated models and human decision-makers.
Principle 26 – Copying creates simplified representations of complex analytical relationships for review and communication.
Expected Outcome
Preserved analytical performance
Greater model interpretability
Better auditor challenge
More defensible analytical conclusions
Decision Indicators
Auditors cannot explain why models classify transactions as high risk.
Reviewers accept analytical outputs primarily because of technical complexity.
Management disputes findings because analytical reasoning is unclear.
Teams avoid advanced analytics because results are difficult to communicate.
Model outputs become substitutes for underlying audit evidence.