The banking sector has witnessed rapid development in the use of artificial intelligence (AI), particularly in credit management and credit risk assessment. AI technologies enable banks to analyze large volumes of financial and non-financial data, identify patterns, and generate indicators that support the assessment of customers' creditworthiness and the prediction of potential default. This article aims to highlight the role of AI in developing credit management and moving toward smarter approaches to credit risk assessment. The importance of this approach lies in supporting credit decisions, improving the speed of financing applications, and helping banks identify risks at an early stage. However, the use of AI also involves challenges related to data quality, privacy, bias, and the interpretability of intelligent models. Keywords: Artificial Intelligence, Credit Management, Credit Risk, Credit Assessment, Machine Learning, Banking. Introduction Bank credit is one of the most important activities carried out by banks and represents an important source of financing for economic activities. However, lending involves risks arising from the possibility that borrowers may be unable to meet their financial obligations. Therefore, accurately assessing creditworthiness before granting loans, as well as continuously monitoring borrowers after lending, are essential elements of effective credit management. With rapid technological development, traditional methods alone may not be sufficient to deal with the increasing volume and variety of available data. Artificial intelligence has provided analytical tools capable of processing large amounts of information and identifying indicators that can support credit decisions. Consequently, the move toward smart credit risk assessment has become an important application of artificial intelligence in the banking sector. First: The Concept of Artificial Intelligence in Credit Management Artificial intelligence in credit management refers to the use of algorithms and intelligent models, particularly machine learning techniques, to analyze customer and loan-related data in order to support risk assessment and credit decision-making. The use of AI is not limited to the stage of granting credit. It can extend across the entire credit cycle, beginning with the evaluation of a customer's application, followed by risk classification, and continuing with loan monitoring and the identification of potential default indicators. Second: Smart Credit Risk Assessment Smart credit assessment involves analyzing a range of information related to customers, such as income, financial obligations, credit history, repayment behavior, previous loans, and other information that banks can legally and appropriately obtain. Intelligent models analyze these data to identify patterns associated with different levels of risk. Based on the analysis, banks can obtain indicators that help estimate the probability of default and support appropriate decisions according to their credit policies. This approach can also improve the speed of the assessment process because intelligent models are capable of processing large amounts of data within a short period. Third: The Role of Artificial Intelligence in Credit Risk Management Artificial intelligence can contribute to credit risk management in several ways, including: Creditworthiness Assessment: Supporting banks in evaluating customers' ability to repay their obligations. Default Prediction: Identifying indicators associated with a potential failure to repay. Continuous Monitoring: Monitoring changes in customers' behavior after loans are granted. Fraud Detection: Identifying unusual patterns in financial transactions Credit Decision Support: Providing analytical information that assists credit officers in making decisions. Fourth: The Impact of Smart Assessment on Banking Operations The use of artificial intelligence can contribute to reducing the time required to analyze credit applications and improving banks' ability to process large amounts of data. It can also support the early identification of potential credit risks. Furthermore, AI can help banks move from approaches that rely heavily on traditional analysis toward data-driven and predictive models, while maintaining the role of human expertise and banking controls in the final decision-making process. Fifth: Challenges of Using Artificial Intelligence Despite its potential benefits, the application of AI in credit management faces several challenges. One of the most important is data quality, as inaccurate or incomplete data may lead to unreliable results. Protecting banking data and customer privacy is another major concern. In addition, banks need to address potential bias that may arise from the data used to develop and train intelligent models. Another challenge is model explainability. Banks should be able to understand the factors that lead to classifying a customer within a particular risk level, especially when credit decisions have significant financial consequences. Sixth: Requirements for Successful Smart Assessment Successful use of AI in credit risk assessment requires accurate and reliable data, appropriate technological infrastructure, and qualified banking staff capable of using and interpreting intelligent models. It also requires effective governance and monitoring policies, regular evaluation of model performance, and continuous human oversight. AI results should therefore support, rather than completely replace, professional judgment and institutional credit policies. Seventh: The Al-Mustaqbal of Artificial Intelligence in Banking Credit The future of credit management is moving toward greater reliance on intelligent analysis and risk prediction as machine learning and data analytics continue to develop. AI is expected to play an increasingly important supporting role across different stages of the credit cycle. However, the success of this development will depend not only on technology but also on banks' ability to balance technological innovation with data quality, customer protection, human oversight, and compliance with banking regulations. Conclusion Artificial intelligence represents one of the modern approaches that can contribute to the development of banking credit management through data analysis, creditworthiness assessment, default prediction, and loan risk monitoring. Smart credit risk assessment provides banks with an opportunity to develop traditional credit practices and rely more extensively on data-driven analysis. However, achieving the full benefits of these technologies requires high-quality data, customer privacy protection, appropriate measures to reduce bias, model explainability, and continuous human oversight of credit decisions. Recommendations Enhance the use of artificial intelligence technologies in credit risk management. Improve the quality and reliability of banking databases. Provide training for banking employees on the use and interpretation of intelligent models. Establish clear controls to protect customer data and privacy. Regularly review intelligent models to ensure the reliability of their results. Combine AI-based analysis with professional banking expertise when making credit decisions