Confusion matrix for CART.

The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by banks from various communication channels. In the research carried out in the work, an attempt was made to obtain an answer to the question whether the data collected by banks can also be used to determine the degree of consumer interest in this type of service. This is important because the identification of customers interested in the service will allow banks to direct a properly prepared message to a selected group of addressees, increasing the effectiveness of their promotional activities. The aim of the article is to construct and examine the effectiveness of predictive models of consumer acceptance of robo-advisory services provided by banks. Based on the authors’ survey on the use of artificial intelligence technology in the banking sector in Poland, in this article we construct tree-based models to predict customers’ attitudes towards using robo-advisory in banking services using, as predictors, their socio-demographic characteristics, behaviours and attitudes towards modern digital technologies, experience in using banking services, as well as trust towards banks. In our study, we use selected machine learning algorithms, including a decision tree and several tree-based ensemble models. We showed that constructed models allow to effectively predict consumer acceptance of robo-advisory services.

银行业日益认识到实施智能投顾（robo-advisory）的必要性。该服务的落地可提升银行运营效率、优化客户服务品质，并强化银行作为创新型金融机构的品牌形象。智能投顾会使用银行从各类沟通渠道获取的客户相关数据、客户行为与偏好数据。本研究尝试解答如下问题：银行所收集的此类数据是否也可用于判定消费者对该类服务的兴趣程度。这一点至关重要，因为识别出对该服务感兴趣的客户，能够让银行向目标受众推送定制化的营销信息，提升推广活动的有效性。本文的研究目标是构建并检验银行智能投顾服务消费者接受度预测模型的效能。基于作者针对波兰银行业人工智能技术应用情况开展的调研，本文构建了基于树结构的模型，以客户的社会人口统计学特征、对现代数字技术的行为与态度、银行服务使用经验以及对银行的信任度作为预测变量，预测客户对银行智能投顾服务的使用态度。本研究选用了若干机器学习算法，包括决策树以及多种基于树的集成学习模型。研究表明，所构建的模型可有效预测消费者对智能投顾服务的接受度。