Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built using accounting-based financial ratios. This study proposes a bankruptcy prediction model for SMEs that uses transactional data and payment network–based variables under a scenario where no financial (accounting) data are required. Offline and online test results both confirmed the predictive capability and economic benefit of transactional data–based variables. However, incorporating those features in predictive models produces high dimensional problems, which deteriorates model interpretability and increases feature acquisition costs. Thus, we propose a two-stage multiobjective feature-selection method that optimizes the number of features as well as model classification performance. The results showed that the proposed model achieved similar classification performance while greatly reducing the cardinality of the feature subset. Finally, the feature importance evaluation for features in the optimal subset confirmed the importance of transactional data and payment network-based variables for bankruptcy prediction.

许多中小型企业的破产预测模型都是使用基于会计的财务比率来建立的。这项研究提出了一种针对中小企业的破产预测模型，该模型在不需要财务（会计）数据的情况下使用交易数据和基于支付网络的变量。离线和在线测试结果均证实了基于交易数据的变量的预测能力和经济效益。但是，将那些特征合并到预测模型中会产生高维问题，这会降低模型的可解释性并增加特征获取成本。因此，我们提出了一种两阶段的多目标特征选择方法，该方法可以优化特征数量以及模型分类性能。结果表明，提出的模型具有相似的分类性能，同时大大降低了特征子集的基数。最后，对最佳子集中的特征进行特征重要性评估，证实了交易数据和基于支付网络的变量对于破产预测的重要性。