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Title: Using precedents to identify top management fraud : the study of a case-based learning and reasoning model.
Author: Curet, Olivier Louis.
ISNI:       0000 0001 3400 5238
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 1996
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This thesis discusses how best to design, implement and evaluate a Case-Based Learning and Reasoning (CB-LR) model to assist accountants in identifying Top Management Fraud (TMF). There is no structured knowledge about TMF in the fonn of rules, only cases encountered by experienced auditors. The changing economic, financial and social environment has produced more fraud which at the same time has become increasingly complex to identify and isolate. Previous research shows that fraud has evaded auditors, and highlights a need for new computer-based learning and reasoning paradigms in this domain. Case-Based Reasoning (CBR) has been considered as an approach to building knowledge systems which involves reasoning about the current situation on the basis of pinpointing and resuscitating past instances. As with artificial intelligence in general, there is no standard readymade CBR method suitable for any domain or application: the challenge in CBR is to come up with methods that are suitable for problem-solving and learning in particular subject domains and for particular application environments. INCASE, a Top Management Fraud diagnostic application, has been designed with a specific methodology derived from Schank and Riesbeck's principles. INCASE works on the basis of the interviewees' concerns so that it can act as a 'stimulus agent' for decision support. The intention is for auditors to use it proactively in a conversational mode, focusing on learning and reasoning about the problem domain~ hence the use of the CB-LR model. Although evaluation methods relevant to traditional rule-based expert systems have been discussed in the literature, their emphasis on system issues was found to be inappropriate for this CB-LR application. Since there is no agreed and established method for evaluating a casebased tool, 'a new approach is discussed including verification (where the focus is on response accuracy of infonnation retrieval) and validation (where user judgement and satisfaction are key issues). One important component in the evaluation was an in-house questionnaire based on total quality management ideas. Findings from the evaluation of the TMF diagnostic system suggest that case-based learning and reasoning has a valuable part to play in assisting auditing profession in the detection of fraud
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer-based learning; Knowledge systems