Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks

Loan risk evaluation is critical for the safety and expansion of financial institutions, but it poses substantial hurdles owing to the intricacy of the data involved. This paper provides an innovative computational approach, the Particle Swarm Optimization-Excited Binary Grey Wolf Optimization-CatBoost (PSO-EBGWO-CatBoost) method, which is intended to improve loan risk forecast accuracy. The proposed framework uses PSO for optimum feature selection, while EBGWO fine-tunes CatBoost's hyperparameters, resulting in better predictive efficiency. Before using the PSO-EBGWO-CatBoost model, the input dataset is preprocessed to remove outliers and missing values. The model's efficiency was verified using a loan dataset, and the findings showed outstanding results in loan risk estimate, with an accuracy of 81.23%, precision of 82.10%, and recall of 80.26%. These findings show that the suggested method greatly outperforms existing strategies, making it an effective instrument for loan risk handling in financial organizations.