This paper shows how transactional bank account data can be used to predict and to prevent financial distress in consumers. Machine learning methods were used to identify the most significant transactional behaviours that cause financial distress. We show that Random Forest outperforms the other machine learning models when predicting the financial distress of a consumer. We obtain that Fees and Interest paid stand out as primary contributors of financial distress, emphasizing the significance of financial charges and interest payments in gauging individuals’ financial vulnerability. Using Local Interpretable Model-agnostic Explanations, we study the marginal effect of transactional behaviours on the probability of being in financial distress and assess how different variables selected across all the data point selection sets influence each case. We also propose prescriptions that can be communicated to the client to help the individual improve their financial wellbeing. This research used data from a major South African bank.

本文展示了如何使用交易银行账户数据来预测和预防消费者的财务困境。机器学习方法被用来识别导致财务困境的最重要的交易行为。我们表明，在预测消费者的财务困境时，随机森林优于其他机器学习模型。我们发现，费用和利息支付是造成财务困境的主要因素，强调了财务费用和利息支付在衡量个人财务脆弱性方面的重要性。使用与局部可解释模型无关的解释，我们研究交易行为对陷入财务困境的概率的边际效应，并评估在所有数据点选择集中选择的不同变量如何影响每个案例。我们还提出可以传达给客户的处方，以帮助个人改善财务状况。这项研究使用了南非一家主要银行的数据。