Intesa Sanpaolo, Torino, Italy.
Università degli Studi di Torino, Torino, Italy.
PLoS One. 2021 Apr 29;16(4):e0250115. doi: 10.1371/journal.pone.0250115. eCollection 2021.
Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a 'hybrid' model, which improves the recall for the task by almost 20 percentage points over the baseline.
贸易信贷是销售企业向其客户提供的一种付款延期。公司通常会通过延迟向供应商付款来应对来自客户的逾期付款，从而在交易网络中产生连锁反应。因此，在违约事件中，贸易信贷是损失传播的潜在载体。这项工作的目的是利用关联企业之间的贸易信贷信息来预测企业即将发生的违约。我们使用一家意大利主要银行的客户公司的独特数据集来调查 2016 年 10 月至 2018 年 3 月期间的公司破产情况。我们开发了一个模型，通过顺序方法捕捉供应链中源自供应链的网络溢出效应对每家公司违约概率的影响：第一模型组件对单个公司特征的输出用于随后的模型，该模型捕获网络溢出。虽然第一部分是预测此类动态的标准计量经济学方法，但网络模块代表了一种研究贸易信贷对违约概率影响的创新方法。该模块着眼于从银行转账中推断出的公司交易网络，以识别公司的贸易伙伴。通过使用从交易网络中提取的几个特征，该模型能够预测很大一部分违约，从而显示出网络信息中隐藏的价值。最后，我们将公司和网络特征与机器学习模型合并，创建一个“混合”模型，该模型通过将基线提高近 20 个百分点，从而提高了任务的召回率。
