A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information

Abstract

The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.