A financial services company has developed an AI model by using AWS.

The verified answer is A. Amazon SageMaker Clarify. The requirement is transparency and explainability for a model that supports loan application decisions. AWS documentation states that Amazon SageMaker Clarify provides tools to explain how machine learning models make predictions. These tools help modelers, developers, and stakeholders understand model characteristics before deployment and debug predictions after deployment. AWS also states that transparency about how ML models arrive at predictions is critical to consumers and regulators, which directly matches the financial-services regulatory requirement in the question.

SageMaker Clarify uses feature attribution methods based on SHAP and Shapley values. AWS explains that Shapley values help determine the contribution that each feature made to model predictions and can be provided for specific predictions and globally for the model as a whole. For a loan application model, this can help explain whether features such as income, credit history, or debt ratio had greater influence on the decision.

Amazon Rekognition is incorrect because it is used for computer vision tasks such as image and video analysis, not explaining tabular loan approval decisions. Amazon Comprehend is incorrect because it is an NLP service for extracting insights from text, such as entities, sentiment, or key phrases. It does not provide general model explainability for loan decision models. Amazon SageMaker Model Monitor is incorrect because it monitors model quality and drift in production, but the question asks for explaining how decisions are made. Therefore, SageMaker Clarify is the correct service.