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                  Y Bao, B Ke, Bin Li (李斌), J Yu, J Zhang: Detecting Accounting Frauds in Publicly Traded US Firms Using a Machine Learning Approach
                  時間:2019-12-02    點擊數:

                  Abstract: 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 method 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: Dechow et al.’s logistic regression model based on financial ratios and Cecchini et al.’ssupport-vector-machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.

                  【摘要】:上市公司財務欺詐是一個世界性的難題。本文基于機器學習方法開發了一套全新的財務欺詐預測模型。我們展示了將領域知識和機器學習方法相結合在模型構建中的價值。雖然本文通過已有的會計理論來選擇模型輸入,但與以前的研究不同,本文采用原始會計數字而非廣泛使用的財務比率。同時,本文采用了一種最強大的集成機器學習方法。為了評估預測模型的性能,本文引入了一套基于排序的績效評估指標,該指標更適合于欺詐預測任務。實證結果顯示,從相同的一組基于理論的原始會計數字開始,提出的欺詐預測模型優于兩個基準模型,即基于財務比率的邏輯回歸模型和采用金融核函數的支持向量機模型,后者的金融核函數用于將原始會計數字映射為比率。

                  Keywords: Fraud Prediction; Machine Learning; Ensemble Learning

                  【關鍵字】:欺詐預測;機器學習;集成學習

                  本文于2019年在線發表于Journal of Accounting Research。該期刊為學院A類獎勵期刊,作者按姓氏字母排序。


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