Credit risk modeling is one of the most critical components of financial risk management, directly influencing lending decisions, capital allocation, and overall financial system stability. Traditional statistical methods such as logistic regression and linear probability models, while widely used, often struggle to capture nonlinearities, dynamic interactions, and the high-dimensional nature of modern financial datasets. The proliferation of big data from transactional histories, social media footprints, and alternative credit signals offers unprecedented opportunities to enhance predictive accuracy and robustness in assessing borrower risk. At the same time, the growing complexity of global financial markets and rising borrower heterogeneity call for tools that can adaptively learn and evolve in real time. Artificial intelligence (AI), particularly machine learning algorithms such as random forests, gradient boosting, and deep neural networks, has emerged as a transformative approach to credit risk modeling. These methods can process large, unstructured, and heterogeneous datasets, extracting hidden patterns that improve default prediction, loss forecasting, and portfolio optimization. Beyond predictive power, AI-driven approaches also enable dynamic risk monitoring, incorporating evolving borrower behaviors and macroeconomic shocks. However, challenges remain regarding algorithmic transparency, fairness, and regulatory compliance, especially in ensuring that AI models do not perpetuate bias or undermine trust in financial institutions. This paper examines the integration of AI-driven credit risk modeling into banking systems, highlighting its contributions to financial stability, efficiency in lending operations, and resilience in stress scenarios. It also discusses emerging governance frameworks that balance innovation with risk management, providing a roadmap for sustainable adoption.