Credit Scoring is one of the banking services that can help with evaluating risks that will impact the stability of the banking industry. This journal presents an innovative approach to credit assessment that makes use of Artificial Intelligence (AI) techniques, particularly credit risk modeling that employs multiple algorithms to determine the optimal way to apply AI to credit assessment in the banking industry. This research simulates real-world banking scenarios using a dataset with a variety of credit-related features and financial parameters. Each AI model is put into practice and then modified based on how well it predicts creditworthiness, lowers default risks, and expedites decision-making processes. The Random Forest model exhibits better prediction in the face of changes in the era of economic and banking digitalization. To overcome any unpredictable changes or qualitative information, this multidimensional approach was selected. By integrating Random Forest into credit scoring assessment, banks can achieve higher prediction accuracy and better risk assessment, resulting in more informed lending decisions. The results show that random forest outperforms others algorithm by 99.85%. Additionally, this journal provides insightful information regarding Artificial Intelligence approaches that can help banks upgrade their assessment processes. By integrating Random Forest into the website that can create more robust prediction models.