A financial institution is deploying the SAP Generative AI Hub to generate personalized customer summaries. They need to ensure ethical and accurate outputs. What actions should the institution…

Question

Options

• ATrain the AI models on historical customer data
• BUse built-in bias detection tools for ethical compliance
• CImplement audit trails for all AI decisions
• DIntegrate the model with SAP Analytics Cloud for insights
• EAutomate the workflow approvals for generated summaries

• A
  72% (31)
• D
  16% (7)
• E
  12% (5)

Explanation

Training on historical customer data (A) ensures the AI understands real customer behavior and produces relevant, personalized summaries rather than generic outputs. Built-in bias detection tools (B) are essential in financial services to meet ethical and regulatory compliance requirements, catching discriminatory patterns before they affect customers. Audit trails (C) are a non-negotiable governance requirement - they create accountability by logging every AI decision, which is critical for regulatory review, dispute resolution, and trust.

Why the distractors are wrong:

• D (SAP Analytics Cloud) adds analytical visualization but doesn't directly address ethical or accuracy concerns for the summaries themselves.
• E (Automate workflow approvals) removes human oversight, which is the opposite of what ethical AI deployment requires - especially in a regulated financial context.

Memory tip: Think TBA - Train, Bias-check, Audit. Ethical AI in finance always needs data quality (A), fairness guardrails (B), and accountability logs (C). Anything that skips human oversight (E) or adds unrelated tooling (D) is a distractor.

No community discussion yet for this question.