Generative AI models in finance often rely on large datasets to learn patterns and generate outputs. If the training data contains embedded biases (e.g., socioeconomic, demographic, or market biases), the generative model can amplify these biases in its outputs. This can lead to skewed or unethical outcomes, such as unfair lending practices or biased investment recommendations.

A is incorrect: While financial data can sometimes be limited or restricted, the primary concern for generative AI is the quality and biases within the data rather than its structured nature. Generative AI can work with both structured and unstructured data.

C is incorrect: This is a general concern in all areas of AI/ML, not specific to generative AI.

D is incorrect: Generative AI excels in processing unstructured data (e.g., natural language, images), making this less of a concern for generative AI compared to other methods.