Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

信用评分是根据申请人的凭据（例如社会地位，财务状况等）计算与申请人相关的风险的过程，它对改善金融业的现金流起着至关重要的作用。但是，信用评分数据集可能具有大量不相关或多余的特征，从而导致较差的分类性能和更高的复杂性。因此，通过删除冗余和不相关的功能可以克服具有大量功能的问题。这项工作强调了特征选择的作用，并提出了一种混合模型，该模型通过利用Binary BAT优化技术将特征选择与新颖的适应度函数相结合，并结合了径向基函数神经网络（RBFN）进行信用评分分类。进一步，