The bankruptcy prediction research domain continues to evolve with the main aim of developing a model suitable for real-world application in order to detect early stages of financial distress of a company. The recent developments in computing, combined with the potential applications of big data technologies and artificial intelligence solutions have already made possible the integration of timely and recent information about business activities in order to monitor the financial health of companies. Therefore, this paper focuses on the predictions made a few months prior to the potential default of a company with the aim of identifying the determinants that signal about the insolvency. The experiments include in-depth analysis of model performances using different dataset configurations.

中文翻译：

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实现短期财务困境检测的自动化：真实案例研究

破产预测研究领域不断发展，其主要目的是开发适合实际应用的模型，以检测公司财务困境的早期阶段。计算的最新发展，加上大数据技术和人工智能解决方案的潜在应用，已经使整合有关业务活动的及时和最新信息成为可能，以监控公司的财务状况。因此，本文重点关注在公司潜在违约前几个月做出的预测，目的是确定表明破产信号的决定因素。实验包括使用不同数据集配置对模型性能进行深入分析。