In recent years there has been a growing interest in analyzing human behavioral data generated by new technologies. One type of digital footprint that is universal across the world, but that has received relatively little attention to date, is spending behavior.

In this paper, using the transaction records of 1306 bank customers, we investigated the extent to which individual-level psychological characteristics can be inferred from bank transaction data. Specifically, we developed a more comprehensive feature space using: (1) overall spending behavior (i.e. total number and total amount of transaction), (2) temporal spending behavior (i.e. variability, persistence, and burstiness), (3) category-related spending behavior (i.e. diversity, persistence, and turnover), (4) customer category profile, and (5) socio-demographic information. Using these features, we first explore their association with individual psychological characteristics, we then analyze the performances of the different feature families and finally, we try to understand to what extent psychological characteristics from spending records can be inferred.

Our results show that inferring the psychological traits of an individual is a challenging task, even when using a comprehensive set of features that take temporal aspects of spending into account. We found that Materialism and Self-Control could be inferred with relatively high levels of accuracy, while the accuracy obtained for the Big Five traits was lower, with only Extraversion and Neuroticism reaching reasonable classification performances.

Hence, for traits like Materialism, Self-control, Extraversion, and Neuroticism our findings could be used to improve psychologically-informed advertising strategies for specific products as well as personality-based spending management apps and credit scoring approaches.

近年来，人们对分析新技术产生的人类行为数据的兴趣日益浓厚。消费行为是一种在世界范围内普遍使用但至今很少受到关注的数字足迹。

在本文中，我们使用1306名银行客户的交易记录，调查了可以从银行交易数据中推断出个人层面的心理特征的程度。具体来说，我们使用以下方法开发了更全面的功能空间：（1）总体支出行为（即交易总数和总金额），（2）时间支出行为（即变异性，持久性和突发性），（3）与类别相关支出行为（即多样性，持续性和周转率），（4）客户类别资料和（5）社会人口统计信息。利用这些特征，我们首先探索它们与个体心理特征的关联，然后分析不同特征家族的表现，最后，

我们的结果表明，即使使用考虑到消费时间方面的全面功能，推断个人的心理特征也是一项艰巨的任务。我们发现，可以相对较高的准确性推断出唯物主义和自我控制，而针对“五个五”特质获得的准确性较低，只有“外向性”和“神经质”才能达到合理的分类表现。

因此，对于唯物主义，自我控制，性格外向和神经质等特质，我们的发现可以用于改善特定产品以及基于个性的支出管理应用程序和信用评分方法的基于心理的广告策略。