Ensuring data privacy and security due to the sensitivity of financial data. 2. Addressing ethical considerations to avoid biases and discriminatory practices. 3. Interpreting complex machine learning models and understanding their decision-making processes. 4. Ensuring regulatory compliance to maintain trust and transparency. 5. Accessing high-quality and relevant data for training accurate ML models. 6. Acquiring and retaining a skilled workforce with expertise in both finance and machine learning.