一种数据驱动的可解释案例推理金融风险检测方法
A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection
摘要：
近年来，人工智能方法的迅速发展使得其在各种金融风险预测中得到了广泛的应用。本研究提出一种新的可解释案例推理（CBR）方法，而不需要丰富的金融风险专业知识。与其他黑盒算法相比，可解释CBR系统允许对结果进行自然的经济解释。事实上，实证结果强调了CBR系统在预测金融风险方面的可解释性，这对金融公司及其客户都至关重要。此外，我们的研究结果表明，所提出的自动设计CBR系统具有良好的预测性能相比，其他人工智能方法，克服了主要的缺点，高度依赖于一个标准的CBR系统的先验领域知识的相应领域。
英文摘要：
The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.
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