As an ML engineer at a bank, you've developed a binary classification model using Google Cloud AutoML Tables to predict whether customers will make loan payments on time, which directly influences loan approval decisions. A customer's loan request was recently rejected, and the bank's risk management department has requested a detailed explanation of the model's decision-making process for this specific case. The bank emphasizes the importance of transparency and accountability in its AI-driven decisions, especially given regulatory compliance requirements. You need to provide a clear, actionable explanation that can be understood by non-technical stakeholders. Which of the following approaches will best meet these requirements? (Choose two options if E is available.)