预测市政当局的财务困境：一种结合领域专家知识的机器学习方法
Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise
AI总结：
本文评估了机器学习模型预测意大利市政当局财务困境的能力，通过将会计司法专家的评估指标纳入特征提取过程，实现了财务困境的早期发现，为社区带来更好的结果。
摘要：
市政当局的财务困境虽然类似于私人公司的破产，但对社区福祉的影响要严重得多。因此，尽早发现赤字至关重要。预测市政当局的财务困境是一项复杂的任务，因为它涉及理解影响市政当局财务健康状况的广泛因素。在本文中，我们评估了机器学习模型来预测意大利市政当局的财务困境。会计司法专家在评估财务绩效方面拥有专业知识和经验，他们使用一系列指标进行评估。通过将这些指标纳入特征提取过程，我们可以确保模型考虑到与市政当局财务健康状况相关的广泛信息。本研究结果表明，将机器学习模型与会计司法专家的知识相结合，有助于早期发现财务困境，从而为社区带来更好的结果。
英文摘要：
Financial distress of municipalities, although comparable to bankruptcy of private companies, has a far more serious impact on the well-being of communities. For this reason, it is essential to detect deficits as soon as possible. Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health. In this paper, we evaluate machine learning models to predict financial distress in Italian municipalities. Accounting judiciary experts have specialized knowledge and experience in evaluating the financial performance, and they use a range of indicators to make their assessments. By incorporating these indicators in the feature extraction process, we can ensure that the model is taking into account a wide range of information that is relevant to the financial health of municipalities. The results of this study indicate that using machine learning models in combination with the knowledge of accounting judiciary experts can aid in the early detection of financial distress, leading to better outcomes for the communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
