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首页> 外文会议> >The application of case-based reasoning to assist accountants in identifying top management fraud: a study of the problem domain and the methodological issues in the development, implementation and evaluation of a case-based learning and reasoning tool
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The application of case-based reasoning to assist accountants in identifying top management fraud: a study of the problem domain and the methodological issues in the development, implementation and evaluation of a case-based learning and reasoning tool

机译：基于案例的推理在协助会计人员识别高层管理人员舞弊中的应用：基于案例的学习和推理工具在开发，实施和评估中的问题领域和方法问题的研究

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Although most audit practices have developed many auditing and accounting expert systems, research and progress in this area was relatively slow until the second half of the 80s. One of the reasons for this slow progress was the difficulty of assessing the value added by these systems. Some rule-based applications in audit have been trying to tackle unstructured and dynamic problems. This has caused many design problems-such as knowledge elicitation-and maintenance. The assessment and management of risk may be a topic that exemplifies the need for a non-traditional approach to knowledge management. Research in managerial decision-making has shown that, when faced with a complex problem, an expert will often look to analogous problems for possible solutions. Top management fraud (TMF) is one of those fields for which there are neither underlying models nor formal ways of detecting them. This paper is concerned both with the methodological issues of using case based reasoning for unstructured domains-such as TMF-and with the evaluation process of such a model. It is argued that emphasis should be given to the searching-learning loop, a prerequisite for case-based learning and reasoning systems (CBLR).
机译：尽管大多数审计实践已经开发了许多审计和会计专家系统，但是直到80年代后半叶，该领域的研究和进展仍相对缓慢。进度缓慢的原因之一是难以评估这些系统所增加的价值。审计中一些基于规则的应用程序一直在尝试解决非结构化和动态问题。这导致了许多设计问题，例如知识的获取和维护。风险的评估和管理可能是一个主题，说明了对知识管理采用非传统方法的必要性。对管理决策的研究表明，面对复杂的问题，专家通常会寻找类似的问题来寻求可能的解决方案。最高管理欺诈（TMF）是既没有基础模型也没有正式检测方法的领域之一。本文既关注针对非结构化领域（例如TMF）使用基于案例的推理的方法论问题，也涉及这种模型的评估过程。有人认为应重点关注搜索学习循环，这是基于案例的学习和推理系统（CBLR）的先决条件。

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