用可解释AI分析财报数据，找出影响企业盈利的关键因素。
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements
- 基于博弈论的可解释AI方法识别敏感财务特征
- 在2013-2022年意大利上市公司数据上验证效果
- 符合欧盟AI法规，适合金融风控与审计场景
影响企业绩效的经济变量具有高度关联性，预测企业盈利趋势极具挑战。现有方法多依赖简单模型和财务比率，难以捕捉复杂交互影响。本文利用机器学习技术处理来自AIDA数据库（2013–2022年意大利上市公司数据）的原始财报数据，对比多种模型，并依据欧洲AI法规，引入可解释性技术辅助分析。特别提出一种基于博弈论的可解释人工智能方法，用于识别最具敏感性的特征，提升结果可解释性。
原文摘要 · Abstract (English)
The interconnected nature of the economic variables influencing a firm's performance makes the prediction of a company's earning trend a challenging task. Existing methodologies often rely on simplistic models and financial ratios failing to capture the complexity of interacting influences. In this paper, we apply Machine Learning techniques to raw financial statements data taken from AIDA, a Database comprising Italian listed companies' data from 2013 to 2022. We present a comparative study of different models and following the European AI regulations, we complement our analysis by applying explainability techniques to the proposed models. In particular, we propose adopting an eXplainable Artificial Intelligence method based on Game Theory to identify the most sensitive features and make the result more interpretable.
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