Current Issue, Volume 24

Doublethink in governmental accounting: development of an RPA to identify inconsistencies in financial reporting Luiz Otavio Schmall dos Santos, Ricardo Lopes Cardoso, Felipe Buchbinder and Miklos Vasarhelyi Published January 2024
Aiming to assess the reliability of governmental accounting under an armchair-audit approach, we develop a framework to compare the financial reports submitted by municipalities to two different agencies: the Ministry of Finance and the respective Court of Accounts. We developed a framework using concepts of RPA in conjunction with OCR to download, extract, organize, and finally compare the reliability of the financial reports submitted by the municipalities. The results indicate that a framework of RPA is helpful to automate many tasks necessary to armchair-audit of municipalities financial reports. The results also indicate that many Brazilian municipalities submit inconsistent data to the monitoring agencies, i.e., the Ministry of Finance and respective Court of Accounts. Additionally, our findings suggest that more computerized entities are less prone to present inconsistencies in their accounting data.
The authorship origins of accounting information systems and emerging technologies research: An analysis of accounting information systems journals Qi Liu, Victoria Chiu and Amelia Annette Baldwin Published March 2024
This article examines the authorship origins of accounting information systems (AIS) and emerging technologies (ET) research from 2004 to 2021 in six journals: Journal of Emerging Technologies in Accounting (JETA), Journal of Information Systems (JIS), International Journal of Accounting Information Systems (IJAIS), International Journal of Digital Accounting Research (IJDAR), Accounting Information Systems Educator Journal (AISEJ), and Intelligent Systems in Accounting, Finance, and Management (ISAFM). This study contributes to the understanding of AIS and ET research by conducting a comprehensive analysis of 1,101 research articles published in these AIS journals by authors’ employer and doctoral country, employer institutions, doctoral institutions, doctoral disciplines, author type, and by AIS and ET classifications. The aim of this study is to identify the historically most productive and influential countries and institutions in the AIS and ET domain and to discover the educational and professional background of AIS and ET researchers, respectively. The findings of this study provide helpful information for job seekers, prospective Ph.D. students, researchers seeking co-authorship, and those interested in this literature and serve as a valuable supplement to the existing bibliometric analysis of AIS literature.
Anomaly detection with the density based spatial clustering of applications with noise (DBSCAN) to detect potentially fraudulent wire transfers Yongbum Kim and Miklos Vasarhelyi Published May 2024
Most anomaly detection models are developed by using expert system methods that mimic human experts. The process to capture the expertise honed by fraud examiners is complicated and practically challenging, often resulting in suboptimal models. This study proposes a clustering-based model that captures hidden characteristics of potentially fraudulent wire transfers with less human intervention and expertise. Clustering methods classify and group observations with similar characteristics, excluding anomalies from major clusters. The choice of a clustering method and its parameters is often subjective and significantly affects a set of resulting clusters. In order to reduce the subjectivity of a clustering method while retaining its strength, this study proposes a clustering model with Density Based Spatial Clustering of Applications with Noise (DBSCAN) to detect potentially fraudulent wire transfers of an insurance company. The results show that the DBSCAN models identifies hidden relationships between the variables not only included but also excluded for the modeling with noise wire transfers while less human intervention is needed for clustering parameter selections.