Small and Medium-sized Enterprises (SMEs) are known to play a vital role in economic growth, employment, and innovation. However, they tend to face significant challenges in accessing credit due to limited financial histories, collateral constraints, and exposure to macroeconomic shocks. These challenges make an accurate credit risk assessment by lenders crucial, particularly since SMEs frequently operate within interconnected firm networks through which default risk can propagate. This paper presents and tests a novel approach for modelling the risk of SME credit, using a unique large data set of SME loans provided by a prominent financial institution. Specifically, our approach employs Graph Neural Networks to predict SME default using multilayer network data derived from common ownership and financial transactions between firms. We show that combining this information with traditional structured data not only improves application scoring performance, but also explicitly models contagion risk between companies. Further analysis shows how the directionality and intensity of these connections influence financial risk contagion, offering a deeper understanding of the underlying processes. Our findings highlight the predictive power of network data, as well as the role of supply chain networks in exposing SMEs to correlated default risk.

翻译：中小企业（SMEs）在经济增长、就业和创新方面发挥着至关重要的作用。然而，由于财务历史有限、抵押品约束以及易受宏观经济冲击的影响，它们在获取信贷方面往往面临重大挑战。这些挑战使得贷款机构进行准确的信用风险评估变得至关重要，特别是考虑到中小企业经常在相互关联的企业网络中运营，违约风险可能通过这种网络传播。本文提出并测试了一种新颖的中小企业信用风险建模方法，该方法使用了由一家知名金融机构提供的独特的大型中小企业贷款数据集。具体而言，我们的方法采用图神经网络（Graph Neural Networks），利用源自企业间共同所有权和金融交易的多层网络数据来预测中小企业违约。我们证明，将这些信息与传统结构化数据相结合，不仅提高了申请评分的性能，而且明确地建模了公司间的传染风险。进一步的分析揭示了这些连接的方向性和强度如何影响金融风险传染，从而提供了对底层过程更深入的理解。我们的研究结果凸显了网络数据的预测能力，以及供应链网络在使中小企业面临关联违约风险中的作用。