Business Risk Prediction and Management Based on Artificial Intelligence by Applying Machine Learning Algorithms to Increase Business Flexibility and Social Stability: Banking Credit Risk Approach

Credit risk assessment is vital for financial sustainability in the banking industry. This paper explores the application of artificial intelligence techniques in improving business risk prediction, particularly focusing on the risk of credit default. We constructed and tested five machine learning models – Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Neural Network – against the dataset of historical loans. Each model was subjected to performance evaluation through standard classification metrics (accuracy, precision, recall, F1 score) and visual tools (confusion matrices, error distribution plots, and predicted vs. actual charts). The findings indicate that the Random Forest method was superior to the other methods as it produced the highest prediction accuracy and the model with the best balanced performance. The ensemble method did perform better but it was not at the same level as Random Forest. These results showcase that the application of machine learning in lending is great. With these models, banks can assess risk factors of given loans much more accurately. Better predictive accuracy will definitely greater assistance in riskier lending and consequently, aid in social and economic stabilization by building up financial security.