Detecting Accounting Fraud in Publicly Traded US Firms Using a Machine Learning Approach
Detecting Accounting Fraud in Publicly Traded US Firms Using a Machine Learning Approach
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使用机器学习方法检测美国上市公司的会计欺诈
DOI：
10.1111/1475-679x.12292
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发表时间：
2020-01-19
影响因子：
4.4
通讯作者：
Zhang, Jie
中科院分区：
文献类型：
--
作者：
Bao, Yang;Ke, Bin;Zhang, Jie
我们使用机器学习方法开发了一个最先进的欺诈预测模型。我们展示了在模型构建中结合领域知识和机器学习方法的价值。我们根据现有的会计理论选择我们的模型输入，但我们与以前的会计研究不同，使用原始会计数字而不是财务比率。我们采用了最强大的机器学习方法之一集成学习，而不是常用的逻辑回归方法。为了评估欺诈预测模型的性能，我们引入了一个新的性能评估指标，通常用于排名问题，更适合于欺诈预测任务。从一组相同的理论驱动的原始会计数字开始，我们表明，我们的新的欺诈预测模型优于两个基准模型的大幅度提高：Dechow等人。基于财务比率的逻辑回归模型，Cecchini等人。支持向量机模型的金融内核，将原始会计数字映射到一组更广泛的比率。
We develop a state-of-the-art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory-motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support-vector-machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.