Using data-driven methods to detect financial statement fraud in the real scenario

IF 4.1 3区 管理学 Q2 BUSINESS
Ying Zhou , Zhi Xiao , Ruize Gao , Chang Wang
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引用次数: 0

Abstract

This study seeks to explore the potential of data-driven methods for developing a financial statement fraud prediction model. We emphasize that building a fraud prediction model that can be used to detect fraud in real-world applications should receive attention from researchers. However, the severe class imbalance issue and the complex nature of fraudulent activities make it a rather challenging task. To address these problems, we apply the combinations of different sampling techniques and tree-based ensemble classifiers to an extensive set of raw financial statement data. The results show that the models using an extensive set of raw financial data, undersampling techniques and boosting tree classifiers are superior in fraud detection. Moreover, several features without a priori knowledge are identified to be important for fraud prediction models by feature importance evaluation. Accordingly, this study provides a methodological guide for designing fraud prediction models for real-world applications and serves as a preliminary step of the knowledge discovery process to complement fraud detection knowledge systems.

使用数据驱动方法检测真实场景中的财务报表欺诈行为
本研究旨在探索数据驱动方法在开发财务报表欺诈预测模型方面的潜力。我们强调,建立一个可用于检测现实世界应用中欺诈行为的欺诈预测模型应得到研究人员的重视。然而,严重的类不平衡问题和欺诈活动的复杂性使其成为一项颇具挑战性的任务。为了解决这些问题,我们将不同的抽样技术和基于树的集合分类器组合应用于大量的原始财务报表数据。结果表明,使用大量原始财务数据、欠采样技术和提升树分类器的模型在欺诈检测方面更胜一筹。此外,通过特征重要性评估,还确定了一些不需要先验知识的特征对欺诈预测模型非常重要。因此,本研究为设计真实世界应用中的欺诈预测模型提供了方法指导,并作为知识发现过程的第一步,对欺诈检测知识系统进行了补充。
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来源期刊
CiteScore
9.00
自引率
6.50%
发文量
23
期刊介绍: The International Journal of Accounting Information Systems will publish thoughtful, well developed articles that examine the rapidly evolving relationship between accounting and information technology. Articles may range from empirical to analytical, from practice-based to the development of new techniques, but must be related to problems facing the integration of accounting and information technology. The journal will address (but will not limit itself to) the following specific issues: control and auditability of information systems; management of information technology; artificial intelligence research in accounting; development issues in accounting and information systems; human factors issues related to information technology; development of theories related to information technology; methodological issues in information technology research; information systems validation; human–computer interaction research in accounting information systems. The journal welcomes and encourages articles from both practitioners and academicians.
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