Assessing credit risk has become a key activity in risk management and is particularly relevant for lenders, investors, and overbuilding markets. The purpose of this study is to determine the extent to which new deep learning methods can change credit risk modelling with large data and algorithms because they may facilitate the performance of predictions and risk mitigation techniques. Using advanced neural networks such as CNNs and RNNs, this study analyzes authentication adequacy and default prediction models based on key borrower characteristics, their financial history, and relevant macroeconomic conditions. Deep learning models overcome the limitations of classical statistical methods and improve performance for much more complex tasks, such as classification and regression, in assessing credit risk. Furthermore, solutions to deep learning explanatory difficulties can be developed through the use of XAI methods. Such approaches make it possible for all stakeholders to utilize the results of the model, which, in turn, makes systems more transparent and trusted rather than using incomprehensible artificial intelligence. This study demonstrates how to allocate credit to optimize the default rate; in other words, it demonstrates how to build stronger financial systems. It expresses the significance of AI regarding the use of information within a changing business environment. This study facilitates emerging AI-driven finances by developing the underlying framework of credit risk analysis and its impact on the business world. In so doing, the paradigm of assessing credit risk is altered.