The presence of artificial intelligence in financial decision-making points to the requirement of the frameworks characterized by both maximised predictive per-formance and adherence to the regulations. We propose a federated learning framework with built-in compliance layers that decreases the bias in the algorithms to 63% in comparison with deep learning algorithms [1]. This method of analyzing Shapley value using dynamic regulatory checks and real-time has been reported with 98.1% area under the curve performance in predicting credit risk and full compliance with GDPR and Basel III regulations. The system shows 40 per cent reduced convergence time over extant bias-corrected systems due to masking techniques of gradients. Fairness violations are reduced by 58 in 12 financial institutions experiment, with no engagement in predictive accuracy (p < 0.001). This paper sets new standards of Ethical AI implementations in automated lending technology, which give practical recommendations to law enforcement and programmers involved in financial technology.