Designing and evaluating a case-based learning and reasoning agent in unstructured decision making

This paper discusses the application of case-based reasoning to assist accountants in identifying top management fraud (TMF). There is no coherent, structured knowledge about TMF in the form of models, 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 catch. Previous research shows that fraud has evaded auditors, and highlights a need for new computer-based learning and reasoning paradigms in this domain. In particular, this paper concentrates on methodological issues in the development, implementation and evaluation of a case-based learning and reasoning (CB-LR) tool.