The growing complexity of financial markets and the acceleration of data availability have highlighted the limitations of traditional credit scoring systems, which often rely on static information and lagging indicators. This study investigates the integration of artificial intelligence with high-frequency data to enable real-time credit risk monitoring. Using a dataset comprising over 150,000 credit application records, the research compares the performance of three machine learning models: logistic regression, gradient boosting (XGBoost), and deep neural networks. Emphasis is placed on evaluating these models under a simulated real-time environment using rolling-window updates, replicating the continuous flow of new borrower information. The results reveal that gradient boosting consistently outperforms the other models across multiple metrics, including AUC, F1 score, and recall, while also maintaining accuracy over time. Feature importance analysis identifies debt-to-income ratio, credit history length, and loan amount as the most predictive indicators of credit default. The study further demonstrates the practical applicability of AI in real-time settings by simulating model performance over multiple 30-day intervals, showcasing the resilience and adaptability of the models, particularly XGBoost. This research contributes to the field by providing a replicable framework for deploying real-time credit risk models and offers evidence that high-frequency data, when paired with interpretable machine learning techniques, enhances both the speed and accuracy of credit evaluations. These findings have broad implications for lenders, regulators, and technology providers seeking to modernize risk assessment in an increasingly data-driven financial landscape.