重塑:通过加强SHapley加性解释解释财务报表审计中的会计异常

Ricardo Müller, Marco Schreyer, Timur Sattarov, Damian Borth
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引用次数: 2

摘要

在财务报表审计中,发现会计异常是一个反复出现的挑战。最近,人们提出了基于深度学习(DL)的新方法,如自动编码器神经网络(AENNs),用于审计大量报表的基础会计记录。然而,由于它们有大量的参数,这些模型表现出固有的不透明的缺点。同时,由于审计师必须合理地解释和证明其审计决策的合理性,模型内部工作原理的隐藏往往会阻碍其在财务审计中的实际应用。如今,各种可解释的人工智能(XAI)技术已经被提出来解决这一挑战,例如,SHapley加性解释(SHAP)。然而,在财务审计中经常应用的无监督深度学习中,这些方法在编码变量的水平上解释模型输出。因此,人工审计师通常很难理解aenn的解释。为了减轻这个缺点,我们提出了重建错误SHapley加性解释扩展(重塑),它在聚合属性级别上解释模型输出。此外,我们还介绍了一个评估框架,以比较XAI方法在审计中的多功能性。我们的实验结果显示,与最先进的基线相比,在多种解释中重塑结果的经验证据。我们设想这种属性级解释是在财务审计中采用无监督深度学习技术的必要下一步。
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RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
Detecting accounting anomalies is a recurrent challenge in financial statement audits. Recently, novel methods derived from Deep-Learning (DL) such as Autoencoder Neural Networks (AENNs) have been proposed to audit the large volumes of a statement’s underlying accounting records. However, due to their vast number of parameters, such models exhibit the drawback of being inherently opaque. At the same time, the concealing of a model’s inner workings often hinders its real-world application in financial audits, since auditors must reasonably explain and justify their audit decisions. Nowadays, various Explainable AI (XAI) techniques have been proposed to address this challenge, e.g., SHapley Additive exPlanations (SHAP). However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables. As a result, the explanations of AENNs are often hard to comprehend by human auditors. To mitigate this drawback, we propose Reconstruction Error SHapley Additive exPlanations Extension (RESHAPE), which explains the model output on an aggregated attribute level. In addition, we introduce an evaluation framework to compare the versatility of XAI methods in auditing. Our experimental results show empirical evidence that RESHAPE results in versatile explanations compared to state-of-the-art baselines. We envision such attribute-level explanations as a necessary next step in the adoption of unsupervised DL techniques in financial auditing.
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