Research on the Construction of Personal Credit Risk Assessment Index System based on PCA

Inhalt:
Building a scientific and comprehensive personal credit risk assessment system is the key to accurately assessing personal credit risk. This paper selects the credit data of the Lending Clue online loan platform in the third quarter of 2017 for empirical research, uses principal component analysis (PCA) to screen credit evaluation indicators, establishes a support vector machine (SVM) model and an artificial bee colony algorithm to optimize the SVM parameter model (ABC-SVM) for verification. The results show that the principal component analysis method can effectively reduce the dimension, eliminate irrelevant variables and redundant variables, and improve the prediction accuracy of personal credit evaluation.