Comparison of machine learning models in credit risk assessment

Abstract

Crdit risk plays an important rol in financ. In a sns, crdit risk rflcts th stability of financial institutions and th protction of invstors. Du to th impact of th pidmic on th world conomy, w nd to rassss th crdit risk. For a long tim, machin larning and dp larning in statistics hav bn vry ffctiv in prdicting crdit risk. But in machin larning and dp larning, whn prdicting crdit risk, w citd four modls, namly XGBoost, Dcision Tr, Random Forst and Convolutional Nural Ntwork (CNN) modls. Analyz th advantags and disadvantags of ths modls: XGBoost can optimiz th tr modl and prvnt ovrfitting through rgularization, but XGBoost has poor intrprtability. Dcision Tr has strong xplanatory powr, but it is prone to overfitting. Compared with th dcision tr, Random Forsts incras accuracy and rduc th probability of overfitting, but Random Forsts consum mor tim and computing rsourcs. Although Convolutional Nural Ntwork has a high accuracy rat, it abandons intrprtability. Thrfor, in our xprimnts, w found that ths modls ar not prfct and hav thir own dfcts. So, in futur rsarch, w will mak an intgratd modl to includ th advantags of th modl and discard som dfcts, so that th intgratd modl will hav bttr gnralization and accuracy.