A financial services firm is implementing AI models to automate fraud detection.

PMI-CPMAI places responsible AI, regulatory compliance, and ethical alignment on equal footing with performance and accuracy, especially in highly regulated sectors like financial services. Fraud detection models often operate on sensitive financial and personal data and can materially impact customers if they are biased or systematically unfair.

The PMI-CPMAI guidance on risk, ethics, and governance emphasizes that project managers must ensure AI systems are evaluated not only on predictive quality but also on fairness, bias, transparency, and explainability. A core expectation is that teams implement bias detection and mitigation strategies across the AI lifecycle: examining training data for representational bias, testing model outputs for disparate impact across customer segments, and applying corrective techniques such as rebalancing, re-weighting, or constraint-based training.

Focusing solely on accuracy (option A) contradicts responsible AI principles and can institutionalize harmful patterns. Using any available data without consent (option C) violates data protection and ethical standards. Assuming compliance without formal verification (option D) fails governance and auditability requirements. By contrast, implementing bias detection and mitigation strategies directly addresses regulatory and ethical concerns, while also supporting robust, trustworthy performance. It operationalizes responsible AI practices in line with PMI-CPMAI expectations, ensuring the fraud models are both effective and compliant.