Using rule induction for knowledge acquisition: An expert systems approach to evaluating material errors and irregularities
| Autor: | Thomas Buttars, Jon Carpenter, Srinivasan Ragothaman |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
Rule induction
Computer science business.industry General Engineering Audit substantive test Sample (statistics) Machine learning computer.software_genre Knowledge acquisition Expert system Computer Science Applications Set (abstract data type) Artificial Intelligence Generalizability theory Data mining Artificial intelligence business computer |
| Zdroj: | Expert Systems with Applications. 9:483-490 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/0957-4174(95)00018-6 |
| Popis: | There has been a significant increase in the magnitude of material errors discovered in financial statements during the 1980s. Auditors, financial analysts, and regulators have shown considerable interest in evaluating and predicting these material errors. This paper describes the development and validation of a prototype expert system, ERRORXPERT, which evaluates material errors and potential fraud. This prototype system is designed to assist auditors at the planning stage in the design of subsequent substantive tests, when material errors and irregularities in the financial statements are probable. A commercial machine learning program was used for rule induction. A set of training examples comprising error and non-error firms was used to generate rules and a separate holdout sample was used to validate the expert system results. The performance of the expert system was also compared to that of a multiple discriminant analysis model using the same data. The results demonstrate that the expert system, ERRORXPERT, outperforms the discriminant model and is a powerful evaluation tool to classify firms into error and non-error categories. The size of the sample used in this study somewhat limits the generalizability of the specific rules. |
| Databáze: | OpenAIRE |
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