The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions.

银行的效率在国家健全金融体系的发展中具有至关重要的作用。数据包络分析（DEA）见证了对银行绩效效率进行建模的日益普及。Such efficiency depends on the appropriate selection of input and output variables. 在文献中，在选择相关变量方面没有达成共识。分歧一直是学术专家之间的一个持续辩论，并且不存在诊断工具来识别可变的错误规格。提出了一种认知分析管理框架，该框架使用三个过程来解决错误规定。认知过程进行了广泛的审查，以确定最常见的变量集。分析过程集成了随机森林方法；具有DEA测量反馈的模拟方法；和Shannon熵选择最佳的DEA模型及其相关变量。最后，管理过程讨论了管理见解，以管理绩效和影响。我们从49个国家/地区收集了2013年至2015年期间303家顶级银行的数据样本。实验模拟结果确定了最佳的DEA模型及其相关变量，并解决了总矿床的分类错误。本文的局限性和未来的研究方向作了总结。实验模拟结果确定了最佳的DEA模型及其相关变量，并解决了总矿床的分类错误。本文的局限性和未来的研究方向作了总结。实验模拟结果确定了最佳的DEA模型及其相关变量，并解决了总矿床的分类错误。本文以局限性和未来的研究方向作为结论。