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Financial Statement Fraud Detection:An Analysis of Statistical and Machine Learning Algorithms

机译：财务报表欺诈检测：统计和机器学习算法的分析

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摘要

This study compares the performance of six popular statistical and machine learning models in detecting financial statement fraud under different assumptions of misclassification costs and ratios of fraud firms to nonfraud firms. The results show, somewhat surprisingly, that logistic regression and support vector machines perform well relative to an artificial neural network, bagging, C4.5, and stacking. The results also reveal some diversity in predictors used across the classification algorithms. Out of 42 predictors examined, only six are consistently selected and used by different classification algorithms: auditor turnover, total discretionary accruals, Big 4 auditor, accounts receivable, meeting or beating analyst forecasts, and unexpected employee productivity. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.
机译：这项研究比较了六种流行的统计和机器学习模型在错误分类成本和欺诈公司与非欺诈公司的比率的不同假设下检测财务报表欺诈的性能。结果有些出乎意料地表明，逻辑回归和支持向量机相对于人工神经网络，装袋，C4.5和堆叠而言表现良好。结果还揭示了在分类算法中使用的预测变量的多样性。在检查的42个预测变量中，只有六个被不同的分类算法一致地选择和使用：审计师营业额，全权应计费用，四大审计师，应收账款，达到或超过分析师预测以及意外的员工生产率。这些发现扩展了财务报表欺诈研究，并且可以由从业者和监管者用来改进欺诈风险模型。

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