Finance

Business

Feature selection

Feature (linguistics)

Data mining

World Wide Web

Machine learning

Philosophy

Payment

Artificial intelligence

Bankruptcy prediction

Interpretability

Computer science

Linguistics

Predictive modelling

Bankruptcy

Cardinality (data modeling)

Decision Support Systems

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.

Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection