Research on credit assessment model based on information entropy and LSSVM

Abstract

Credit assessment models are an important basis for financial credit institutions to determine whether to lend or not, so an efficient and accurate credit assessment model is crucial for financial credit institutions. Traditional credit assessment algorithms do not take into account the noise problem caused by the massive amount of credit data, which greatly affects the time complexity and accuracy of credit assessment algorithms. In view of this, this paper proposes a credit assessment method based on information entropy and LSSVM. The method first uses information entropy to assign weights to feature attributes, then sets thresholds on them for feature extraction, and constructs an LSSVM model to evaluate credit data, so as to achieve accurate assessment of credit transaction risk. The experimental results show that the method can effectively reduce the time complexity of the algorithm and improve the accuracy of prediction