A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem

Abstract

Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generates optimized weights which are used to build a linear discriminant function. The experimental results obtained by applying this model on simulated and real datasets prove its high efficiency and evaluate its significant value on credit scoring.