A New Language and Input–Output Hidden Markov Model for Automated Audit Inquiry
| Autor: | Aaron Saiewitz, Pushkin Kachroo, Robyn L. Raschke, Jiheng Huang, Shaurya Agarwal |
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| Rok vydání: | 2020 |
| Předmět: |
Input/output
Grammar Computer Networks and Communications Computer science Calibration (statistics) business.industry media_common.quotation_subject Intelligent decision support system 02 engineering and technology Audit Machine learning computer.software_genre Constructed language Artificial Intelligence Encoding (memory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Hidden Markov model computer media_common |
| Zdroj: | IEEE Intelligent Systems. 35:39-49 |
| ISSN: | 1941-1294 1541-1672 |
| DOI: | 10.1109/mis.2019.2963653 |
| Popis: | This article presents a mathematical coding language to express dynamic interactions between auditors and client personnel. Then, an input-output hidden Markov model is presented that represents clients as well as auditors, and models the coupled system. The calibrated model can be used to design optimal automated auditors, and can also be used to perform analysis of client inquiry responses. A case study is performed using data collected with subjects simulating auditor–client communications in a controlled environment. We also discuss the details of model calibration, validations, and significance of results. |
| Databáze: | OpenAIRE |
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