FisrEbp: Enterprise Bankruptcy Prediction via Fusing its Intra-risk and Spillover-Risk

ABSTRACT In this paper, we propose to model enterprise bankruptcy risk by fusing its intra-risk and spillover-risk. Under this framework, we propose a novel method that is equipped with an LSTM-based intrarisk encoder and GNNs-based spillover-risk encoder. Specifically, the intra-risk encoder is able to capture enterprise intra-risk using the statistic correlated indicators from the basic business information and litigation information. The spillover-risk encoder consists of hypergraph neural networks and heterogeneous graph neural networks, which aim to model spillover risk through two aspects, i.e. hyperedge and multiplex heterogeneous relations among enterprise knowledge graph, respectively. To evaluate the proposed model, we collect multi-sources SMEs data and build a new dataset SMEsD, on which the experimental results demonstrate the superiority of the proposed method. The dataset is expected to become a significant benchmark dataset for SMEs bankruptcy prediction and promote the development of financial risk study further.