- Generative AI’s outputs depend on the quality and recency of its training data.
- Poor-quality or outdated data can lead to unreliable or irrelevant results.
- AI models can become stale if not regularly retrained.
- Lack of updated training may cause AI to miss market shifts or regulatory changes.
- In fast-moving financial markets, stale models can lead to flawed risk assessments and compliance failures.
- Errors or biases in training data can be reflected and even amplified in AI outputs.
- Maintaining data integrity is a continuous operational challenge.