A New Language and Input-Output Hidden Markov Model for Automated Audit Inquiry

Authors

Pushkin Kachroo, University of Nevada, Las VegasFollow
Aaron Saiewitz, University of Nevada, Las VegasFollow
Robyn Raschke, University of Nevada, Las VegasFollow
Shaurya Agarwal, University of Central FloridaFollow
Jiheng Huang, University of Central FloridaFollow

Document Type

Article

Publication Date

1-1-2020

Publication Title

IEEE Intelligent Systems

First page number:

1

Last page number:

8

Abstract

This paper presents a mathematical coding language to express dynamic interactions between auditors and client personnel. Then an Input-Output Hidden Markov Model (IOHMM) 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.

Keywords

Hidden Markov Model; IOHMM; Automated audit; Formal language theory; AI in accounting

Disciplines

Accounting | Electrical and Computer Engineering

Language

English

Repository Citation

Kachroo, P., Saiewitz, A., Raschke, R., Agarwal, S., Huang, J. (2020). A New Language and Input-Output Hidden Markov Model for Automated Audit Inquiry. IEEE Intelligent Systems 1-8.
http://dx.doi.org/10.1109/MIS.2019.2963653