In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to $60$ months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a $60$ month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making.

在公司违约和破产的检测和预测领域，已投入大量精力来使用统计模型和机器学习技术评估财务比率作为预测指标。当根据预测模型的输出为财务决策者提供要采取行动的预测时，此问题变得至关重要。但是，研究表明，此类预测器在短期内足够准确，重点主要针对大中型公司。相反，在本文中，我们着重于中长期破产预测（直至$60$个月），以中小型企业为目标。这项研究的主要贡献是，与最新技术相比，使用机器学习技术在短期（12个月）内的预测准确性有了显着提高，同时还做出了准确的中长期预测（ROC曲线下面积为0.88的量度为$60$月预测范围）。在意大利整个公司范围内进行的大量计算测试突显了所开发方法的效率和准确性，并证明了将其用作开发整个经济体系战略和政策的工具的可能性。考虑到最近的COVID-19大流行，我们展示了如何将我们的方法用作大规模决策的可行工具。