Research on the implication of artificial intelligence in accounting subfields: current research trends from bibliometric analysis, and research directions

All stakeholders recognize the importance of the information provided by various accounting subfields in the decision-making process and managerial activities, on the other hand, with the exponential growth of artificial intelligence, the traditional way of working in accounting has changed, and research about it has been undertaken worldwide, In this context, This study provides a bibliometric analysis of 931 articles which were published from 1990 to 2022 to look for the research trends and most prominent topics and theme addressed in the literature regarding the application of artificial intelligence technologies in five accounting subfields namely Financial Accounting, Management Accounting, Tax Accounting,  Auditing, and Governmental Accounting. Using VOS viewer software, this study contributes to accounting literature by analyzing the current common theme in the literature through visualizing and mapping the occurrence and the co-occurrence of authors’ keywords of 931 articles that address this topic, which will allow us to highlight some less explored avenues of research that can therefore be further explored by scholars. The results show that Financial Accounting is the most commonly researched accounting area explored. The theme most frequently addressed is the detection of financial statement fraud. There were few articles discussing Artificial Intelligence’s implication on Tax Accounting and Government Accounting. Further, the study provided six major areas that have been revealed for future research on this topic: the implication of the Internet of Things, Blockchain and Big Data and the Accounting field, Accounting cybersecurity in the artificial intelligence area, XBRL, and Artificial Intelligence in Accounting.   Keywords: Bibliometric, Accounting subfields, Artificial Intelligence, Vosviewer.                                                                JEL Classification: M4, Q55 Paper type: Theoretical Research All stakeholders recognize the importance of the information provided by various accounting subfields in the decision-making process and managerial activities, on the other hand, with the exponential growth of artificial intelligence, the traditional way of working in accounting has changed, and research about it has been undertaken worldwide, In this context, This study provides a bibliometric analysis of 931 articles which were published from 1990 to 2022 to look for the research trends and most prominent topics and theme addressed in the literature regarding the application of artificial intelligence technologies in five accounting subfields namely Financial Accounting, Management Accounting, Tax Accounting,  Auditing, and Governmental Accounting. Using VOS viewer software, this study contributes to accounting literature by analyzing the current common theme in the literature through visualizing and mapping the occurrence and the co-occurrence of authors’ keywords of 931 articles that address this topic, which will allow us to highlight some less explored avenues of research that can therefore be further explored by scholars. The results show that Financial Accounting is the most commonly researched accounting area explored. The theme most frequently addressed is the detection of financial statement fraud. There were few articles discussing Artificial Intelligence’s implication on Tax Accounting and Government Accounting. Further, the study provided six major areas that have been revealed for future research on this topic: the implication of the Internet of Things, Blockchain and Big Data and the Accounting field, Accounting cybersecurity in the artificial intelligence area, XBRL, and Artificial Intelligence in Accounting.   Keywords: Bibliometric, Accounting subfields, Artificial Intelligence, Vosviewer.                                                                JEL Classification: M4, Q55 Paper type: Theoretical Research&nbsp

Materiality Maps – Process Mining Data Visualization for Financial Audits

Financial audits are a safeguard to prevent the distribution of false information which could detrimentally influence stakeholder decisions. The increasing integration of computer technology for the processing of business transactions create new challenges for auditors who have to deal with increasingly large and complex data. Process mining can be used as a novel Big Data analysis technique to support auditors in this context. A challenge for using this type of technique is the representation of analyzed data. Process mining algorithms usually discover large sets of mined process variants. This study introduces a new approach to visualize process mining results specifically for financial audits in an aggregate manner as materiality maps. Such maps provide an overview about the processes identified in an organization and indicate which business processes should be considered for audit purposes. They reduce an auditor’s information overload and help to improve decision making in the audit process

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. European Journal of Operational Research, 265(2), 399-422. doi:10.1016/j.ejor.2017.07.003Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1Ou-Yang, C., & Winarjo, H. (2011). Petri-net integration – An approach to support multi-agent process mining. Expert Systems with Applications, 38(4), 4039-4051. doi:10.1016/j.eswa.2010.09.066Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2017). Towards evolutionary discovery of typical clinical pathways in electronic health records. Procedia Computer Science, 119, 234-244. doi:10.1016/j.procs.2017.11.181Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems, 14(1), 1-20. doi:10.1016/j.accinf.2012.06.015Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J. P., Blat, J., & Sinatra, R. (2014). An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data. Environment and Planning B: Planning and Design, 41(6), 1113-1131. doi:10.1068/b130047pDe Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235-257. doi:10.1016/j.is.2015.07.003Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Arroyo, R., Yebes, J. J., Bergasa, L. M., Daza, I. G., & Almazán, J. (2015). Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Systems with Applications, 42(21), 7991-8005. doi:10.1016/j.eswa.2015.06.016Popa, M. C., Rothkrantz, L. J. M., Shan, C., Gritti, T., & Wiggers, P. (2013). Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. Pattern Recognition Letters, 34(7), 809-819. doi:10.1016/j.patrec.2012.04.015Kang, L., & Hansen, M. (2017). Behavioral analysis of airline scheduled block time adjustment. Transportation Research Part E: Logistics and Transportation Review, 103, 56-68. doi:10.1016/j.tre.2017.04.004Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.003M. Valle, A., A.P. Santos, E., & R. Loures, E. (2017). Applying process mining techniques in software process appraisals. Information and Software Technology, 87, 19-31. doi:10.1016/j.infsof.2017.01.004Juhaňák, L., Zounek, J., & Rohlíková, L. (2019). Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506. doi:10.1016/j.chb.2017.12.015Sedrakyan, G., De Weerdt, J., & Snoeck, M. (2016). Process-mining enabled feedback: «Tell me what I did wrong» vs. «tell me how to do it right». Computers in Human Behavior, 57, 352-376. doi:10.1016/j.chb.2015.12.040Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321-1331. doi:10.1016/j.chb.2012.02.016Werner, M., & Gehrke, N. (2015). Multilevel Process Mining for Financial Audits. IEEE Transactions on Services Computing, 8(6), 820-832. doi:10.1109/tsc.2015.2457907De Weerdt, J., Schupp, A., Vanderloock, A., & Baesens, B. (2013). Process Mining for the multi-faceted analysis of business processes—A case study in a financial services organization. Computers in Industry, 64(1), 57-67. doi:10.1016/j.compind.2012.09.010Herbert, L., Hansen, Z. N. L., Jacobsen, P., & Cunha, P. (2014). Evolutionary Optimization of Production Materials Workflow Processes. Procedia CIRP, 25, 53-60. doi:10.1016/j.procir.2014.10.010Yim, J., Jeong, S., Gwon, K., & Joo, J. (2010). Improvement of Kalman filters for WLAN based indoor tracking. Expert Systems with Applications, 37(1), 426-433. doi:10.1016/j.eswa.2009.05.047Delafontaine, M., Versichele, M., Neutens, T., & Van de Weghe, N. (2012). Analysing spatiotemporal sequences in Bluetooth tracking data. Applied Geography, 34, 659-668. doi:10.1016/j.apgeog.2012.04.003Frisby, J., Smith, V., Traub, S., & Patel, V. L. (2017). Contextual Computing : A Bluetooth based approach for tracking healthcare providers in the emergency room. Journal of Biomedical Informatics, 65, 97-104. doi:10.1016/j.jbi.2016.11.008Yoshimura, Y., Krebs, A., & Ratti, C. (2017). Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre. IEEE Pervasive Computing, 16(2), 26-34. doi:10.1109/mprv.2017.33Cao, Q., Jones, D. R., & Sheng, H. (2014). Contained nomadic information environments: Technology, organization, and environment influences on adoption of hospital RFID patient tracking. Information & Management, 51(2), 225-239. doi:10.1016/j.im.2013.11.007Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395-414. doi:10.1016/j.ijresmar.2005.09.005Fernandez-Llatas, C., Martinez-Millana, A., Martinez-Romero, A., Benedi, J. M., & Traver, V. (2015). Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318809Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., & Fernandez-Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. Journal of Medical Internet Research, 20(4), e127. doi:10.2196/jmir.8884De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Heyer, L. J. (1999). Exploring Expression Data: Identification and Analysis of Coexpressed Genes. Genome Research, 9(11), 1106-1115. doi:10.1101/gr.9.11.1106Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.00

Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the real-time assessment of digital accounting journal entries. Recently, driven by the advances in artificial intelligence, Deep Learning techniques have emerged in financial auditing to examine vast quantities of accounting data. However, learning highly adaptive audit models in decentralised and dynamic settings remains challenging. It requires the study of data distribution shifts over multiple clients and time periods. In this work, we propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity. Our empirical results, using real-world datasets and combined federated continual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.Comment: 6 pages (excl. appendix), 5 figures, 1 table, preprint version, currently under revie

Process-mining-enabled audit of information systems: Methodology and an application

Current methodologies for Information Systems (ISs) audits suffer from some limitations that could question the effectiveness of such procedures in detecting deviations, frauds, or abuses. Process Mining (PM), a set of business-process-related diagnostic and improvement techniques, can tackle these weaknesses, but literature lacks contributions that address this possibility concretely. Thus, by framing PM as an Expert System (ES) engine, this paper presents a five-step PM-based methodology for IS audits and validates it through a case in a freight export port process managed by a Port Community System (PCS), an open electronic platform enabling information exchange among port stakeholders. The validation pointed out some advantages (e.g. depth of analysis, easier automation, less invasiveness) of our PM-enabled methodology over extant ESs and tools for IS audit. The substantive test and the check on the PCS processing controls and output controls allowed to identify four major non-conformances likely implying both legal and operational risks, and two unforeseen process deviations that were not known by the port authority, but that could improve the flexibility of the process. These outcomes set the stage for an export process reengineering, and for revising the boundaries in the process flow of the PCS

Decentralised and Collaborative Auditing of Workflows

Workflows involve actions and decision making at the level of each participant. Trusted generation, collection and storage of evidence is fundamental for these systems to assert accountability in case of disputes. Ensuring the security of audit systems requires reliable protection of evidence in order to cope with its confidentiality, its integrity at generation and storage phases, as well as its availability. Collusion with an audit authority is a threat that can affect all these security aspects, and there is room for improvement in existent approaches that target this problem. This work presents an approach for workflow auditing which targets security challenges of collusion-related threats, covers different trust and confidentiality requirements, and offers flexible levels of scrutiny for reported events. It relies on participants verifying each other's reported audit data, and introduces a secure mechanism to share encrypted audit trails with participants while protecting their confidentiality. We discuss the adequacy of our audit approach to produce reliable evidence despite possible collusion to destroy, tamper with, or hide evidence