Over a six-year period, employees and managers at Wells Fargo created 3.5 million false deposit and credit card accounts resulting in $4.8 billion in fines. Following this incident, there has been a newfound focus on effective internal controls. The purpose of the current study is to improve misstatement identification by formulating a novel hybrid decision support framework to a) accurately predict financial misstatements and frauds, b) build a parsimonious model by employing a comprehensive variable selection procedure without hurting (in contrast, potentially improving) the model's prediction power, c) uncover the conditional inter-dependencies between the predictors via a Bayesian-belief based probabilistic network, and d) provide stakeholders with a firm-specific MWIC risk score. In an extensive real-life experimental setup, we validate our decision support system and find that the Tree-Augmented Bayesian Belief Network (TAN) model provides high misstatement identification accuracy results when the variables are selected through the Genetic Algorithm (GA) that employs Random Forests (RF) as the classification algorithm (AUC of 0.856 by employing only 5 out of 23 potential variables). Financial experts and stakeholders can use the probabilistic scores provided, while their intuition/incentive should collaborate with prediction models to make final decision on the cases where the model is not confident enough (i.e., when the probabilistic scores are close to 50/50). These insights enable stakeholders to improve the early warning systems for MWIC and financial misstatements and therefore potential frauds.

中文翻译：

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财务报告中错报识别的决策支持框架：一种混合树增强贝叶斯信念方法


在六年的时间里，富国银行的员工和经理创建了 350 万个虚假存款和信用卡账户，导致 48 亿美元的罚款。在这次事件之后，人们开始关注有效的内部控制。本研究的目的是通过制定一种新的混合决策支持框架来改进错报识别，以 a） 准确预测财务错报和欺诈，b） 通过采用全面的变量选择程序构建一个简洁的模型，而不会损害（相反，可能会提高）模型的预测能力，c） 通过基于贝叶斯信念的概率网络揭示预测因子之间的条件相互依赖关系， d） 为利益相关者提供特定于公司的 MWIC 风险评分。在广泛的真实实验设置中，我们验证了我们的决策支持系统，发现当通过采用随机森林 （RF） 作为分类算法的遗传算法 （GA） 选择变量时，树增强贝叶斯信念网络 （TAN） 模型提供了高错报识别准确性结果（AUC 为 0.856，仅使用 23 个潜在变量中的 5 个）。金融专家和利益相关者可以使用提供的概率分数，而他们的直觉/激励应该与预测模型合作，以对模型不够自信的情况（即，当概率分数接近 50/50 时）做出最终决定。这些见解使利益相关者能够改进 MWIC 和财务错报的早期预警系统，从而改进潜在欺诈。