Organizations are facing challenges in aligning their fast-paced AI model updates with slower compliance processes, leading to potential financial risks and penalties due to regulatory oversight. The gap is mainly due to frequent model retraining and adjustments that outpace the quarterly compliance reviews typically conducted, which can cause significant financial burdens when compliance is overlooked. Automated testing frameworks are proposed as a solution, integrating compliance checks directly into the model lifecycle to ensure alignment with regulations such as the CFPB and EU AI Act, which emphasize AI transparency, fairness, and explainability. These frameworks employ validation engines to transform regulatory requirements into executable checks, enabling daily validation and reducing the rush before examinations. Additionally, real-time bias and fairness monitoring, privacy and data protection automation, and continuous compliance testing are crucial components in maintaining regulatory compliance while allowing for rapid model updates. Automated systems also enhance audit trail generation, ensuring comprehensive evidence is readily available for regulatory examinations. Leveraging these automated compliance frameworks, as exemplified by Galileo's platform, can transform financial AI systems into a competitive advantage by enabling continuous monitoring and automated testing to meet evolving regulatory standards.