The integration of Artificial Intelligence (AI) in the banking sector has the potential to significantly enhance decision-making processes, particularly in loan prediction and credit risk assessment. Despite these advancements, traditional loan assessment methods often lead to inaccuracies and biases, increasing the need for more sophisticated AI and machine learning (ML) techniques in the field. This paper reviews the current state of AI applications in the banking industry, focusing on their effectiveness in improving loan predictions while addressing challenges such as data quality, interpretability, and ethical implications. A comprehensive analysis of existing literature reveals the need for transparent and explainable AI models to facilitate stakeholder and customer trust while mitigating biases in financial decision-making. Additionally, the paper identifies critical challenges in AI integration, including the risk of reinforcing societal biases and the potential for fraudulent activities. To address these issues, we propose an AI framework called “EquiLoan” that emphasises the development of an integrated AI solution capable of real-time monitoring, explainability, and bias mitigation. This research aims to contribute valuable insights into the responsible use of AI in the banking industry, promoting fair lending practices and enhancing customer trust, while ensuring ethical considerations are prioritised in financial decision-making.