The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making transparency is of paramount importance. In this paper, we provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics, and we review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important.

翻译：人工智能（AI），尤其是深度学习模型的成功，因其处理海量数据和学习复杂模式的能力，已在各行业得到广泛应用。然而，由于缺乏可解释性，它们在金融和医疗等关键领域的使用引发了重大关切，因为这些领域的决策透明度至关重要。本文针对金融场景中旨在提升深度学习模型可解释性的方法进行了比较性综述。我们根据可解释人工智能方法的相关特征对其进行了分类，并审视了采用这些方法所面临的关切与挑战，同时提出了我们认为适当且重要的未来研究方向。