ABSTRACTWe compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and, at higher cut-offs, the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a “falsely accused” firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples.Data Availability: Data are available from the public sources cited in the text.JEL Classifications: G31; G32; G34; M40.

中文翻译：

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欺诈预测错误的成本


摘要我们将七种欺诈预测模型与基于成本的衡量标准进行比较，该衡量标准将正确预测欺诈实例的好处与错误标记非欺诈公司所承担的成本相结合。我们发现，即使是最好的模型，假阳性与真阳性的比例也会超过 100:1。事实上，大量的误报使得所有七个模型被认为对于审计师来说实施成本太高，即使在误报可能性更大的子样本中也是如此。对于投资者来说，M-Score 和更高截止值的 F-Score 是唯一能提供净收益的模型。对于监管机构来说，有几种模式在经济上是可行的，因为误报成本受到监管机构可以发起的调查数量的限制，并且“被错误指控”的公司根据《信息自由法》拒绝请求时所承受的市场价值损失相对较低（信息自由法）。无论我们考虑欺诈还是两个替代重述样本，我们的结果都是相似的。数据可用性：数据可从文本中引用的公共来源获得。JEL 分类：G31； G32； G34； M40。