This paper proposes a decision support system based on a machine-learned Bayesian network (BN) to predict the success rate of telemarketing calls for long-term bank deposits. Telemarketing is one of the most common interactive techniques of direct marketing, widely used by financial institutions such as banks to sell long-term deposits. In this study, we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit, which is considered an output variable. The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach, a well-known supervised learning algorithm. The impact of each client's attribute on the likelihood of subscribing is predicted. Further, we carry out multiple simulation scenarios using BN’s unique features (forward and backward propagation) to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics.

本文提出了一种基于机器学习贝叶斯网络（BN）的决策支持系统，以预测长期银行存款的电话销售呼叫的成功率。电话销售是直接营销中最常见的交互式技术之一，已被银行等金融机构广泛用于出售长期存款。在这项研究中，我们开发了一种BN模型，该模型可预测潜在客户订阅长期存款（被视为输出变量）的可能性。使用增强的朴素贝叶斯方法（一种著名的监督学习算法）已经确定了客户属性与结果之间的因果关系。可以预测每个客户的属性对订阅可能性的影响。更多，