Automating Vendor Fraud Detection in Enterprise Systems

Kishore Singh, P. Best, J. Mula
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引用次数: 15

Abstract

Fraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii) visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i) a model for proactive fraud detection, ii) methods for visualising user activities in transaction data, iii) a stand-alone MCL-based prototype.
企业系统中供应商欺诈检测的自动化
欺诈是一个每年持续增长的数十亿美元的产业。许多组织在预防和检测欺诈方面准备不足。欺诈检测策略旨在快速有效地识别规避预防措施的欺诈活动。在本文中,我们采用设计科学方法框架来开发一个模型,该模型基于对企业系统审计跟踪中识别的模式或签名的分析来检测供应商欺诈。通过开发原型软件对该概念进行了验证。原型的验证是通过进行一系列的实验来实现的。确认是由审计从业人员的独立评审完成的。本研究的主要发现是:i)自动化常规数据分析提高了审计员的工作效率,减少了识别潜在欺诈行为所花费的时间;ii)Â可视化有助于及时识别潜在的欺诈用户活动。该研究做出了以下贡献:i)Â一个主动欺诈检测模型,ii)在交易数据中可视化用户活动的方法,iii)一个独立的基于mcl的原型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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