Optimizing Corporate Risk Prediction:A Hyperparameter Tuning Approach for Enhanced Performance

Part of being financially stable is to be able to face the uncertainties and make a choice wisely by using corporate risk assessment. The research work discusses the use of LightGBM, XGBoost, and CatBoost in combination to help improve the accuracy of risk prediction for financial analytics. The use of RandomizedSearchCV coupled with 5-fold cross-validation by the model helps it to address problems like overfitting and unequal distribution of data. The trained framework comes to an accuracy of 99.97% and an F1 score of 99.86%, missing only 0.03% from perfection and winning over traditional models of logistic regression. Having hyperparameter optimization in place, the number of false positives goes down by 123 and false negatives by 90, showing how helpful it is. Precision-recall curves indicate where to draw a line between false negatives and false positives. By ensuring that the model is able to grow and is easy to understand, it is suitable for practical risk management. Excellent results are highlighted in quantitative terms, as precision is at 99.78% and recall is 99.94%. This way of working helps financial institutions to anticipate risks and make better decisions. Future work may focus on how deep learning can be used in the macroeconomic field.