Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to mitigate this challenge. However, the effectiveness of such automation may depend on the richness of the training dataset and model efficiency. We have recently developed a novel classifier named random wheel which provides a more interpretable output. In this work, we have used an enhanced version of random wheel to facilitate a trustworthy recommendation for credit card approval process. It not only produces more accurate and precise recommendation but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit card application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which in turn can enhance the efficiency of the credit card approval process.
翻译:核准信用卡申请是银行家通常定期作出的审查性商业决定之一,由于新申请的卡片数量越来越多,而且在最近的大流行病期间,信用卡账单大量未结清,这在今天甚至更加具有挑战性。以前的一些研究显示,使用机器情报使核准程序自动化以减轻这一挑战。然而,这种自动化的有效性可能取决于培训数据集的丰富性和模型效率。我们最近开发了一个名为随机车轮的新型分类师,它提供了更易解释的产出。在这项工作中,我们使用了一种强化的随机车轮,以便利对信用卡核准程序提出值得信赖的建议。它不仅提出了更准确和准确的建议,而且还提供了一种可解释的信任度措施。此外,它解释了每种信用卡申请的机器建议,以及建议信任和解释的可得性,可以使人们更加信任所提供的情报,从而提高信用卡核准程序的效率。