Credit risk evaluation is always the most important factor in determining Customers' credit status in financial institutions. Multi-Attribute Decision-Making (MADM) methods have been widely used in this field. But most of the studies neglect the undeniable impact of time and changes of the credit assessment criteria, their importance and evaluation data over time. On the other hand, developed Dynamic MADM (DMADM) methods often used subjective weighting methods and then applied some aggregation operators to rank alternatives. This paper proposes a new combination of Data Envelopment Analysis (DEA) as a powerful objective weighting method with DMADM as a novel dynamic decision-making method for credit performance evaluation. For this aim, the credit performance criteria were extracted using literature review and experts’ views. Criteria weights were calculated with dynamic DEA common set of weights approach. Then, the applicants are prioritized using five Grey MADM methods (including SAW-G, VIKOR-G, TOPSIS-G, ARAS-G and COPRAS-G). Finally, a new method called Correlation Coefficient and Standard Deviation (CCSD) was used to determine the final aggregated rank. This novel method is applied in order to credit ratings of the clients in the Beekeeping Industry Development Funding Institute IRAN The results indicate that the proposed MADM method, while eliminating the limitations of previous methods, has been able to maintain robustness. Also, the results are highly correlated with the results of previous methods.

信用风险评估始终是确定客户在金融机构中信用状况的最重要因素。多属性决策（MADM）方法已在该领域中广泛使用。但是，大多数研究都忽略了时间和信用评估标准，其重要性和评估数据随时间变化的不可否认的影响。另一方面，已开发的动态MADM（DMADM）方法通常使用主观加权方法，然后应用一些聚合运算符对备选方案进行排名。本文提出了将数据包络分析（DEA）作为强大的目标加权方法与DMADM作为信用评估的一种动态决策方法的新组合。为此目的，使用文献综述和专家意见提取了信用表现标准。使用动态DEA通用权重方法计算标准权重。然后，使用五种灰色MADM方法（包括SAW-G，VIKOR-G，TOPSIS-G，ARAS-G和COPRAS-G）对申请人进行优先级排序。最后，使用一种称为“相关系数和标准偏差”（CCSD）的新方法来确定最终的聚合等级。应用这种新方法是为了在养蜂业发展基金会IRAN中对客户进行信用评级。结果表明，提出的MADM方法在消除了先前方法的局限性的同时，仍然能够保持鲁棒性。而且，结果与先前方法的结果高度相关。TOPSIS-G，ARAS-G和COPRAS-G）。最后，使用一种称为“相关系数和标准偏差”（CCSD）的新方法来确定最终的聚合等级。应用这种新方法是为了在养蜂业发展基金会IRAN中对客户进行信用评级。结果表明，提出的MADM方法在消除了先前方法的局限性的同时，仍然能够保持鲁棒性。而且，结果与先前方法的结果高度相关。TOPSIS-G，ARAS-G和COPRAS-G）。最后，使用一种称为“相关系数和标准偏差”（CCSD）的新方法来确定最终的聚合等级。应用这种新方法是为了在养蜂业发展基金会IRAN中对客户进行信用评级。结果表明，提出的MADM方法在消除了先前方法的局限性的同时，仍然能够保持鲁棒性。而且，结果与先前方法的结果高度相关。在消除先前方法的局限性的同时，也能够保持鲁棒性。而且，结果与先前方法的结果高度相关。在消除先前方法的局限性的同时，也能够保持鲁棒性。而且，结果与先前方法的结果高度相关。