363 research outputs found
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 
Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques
This paper proposes an analytical framework that combines dimension reduction and data mining techniques to obtain a sample segmentation according to potential fraud probability. In this regard, the purpose of this study is twofold. Firstly, it attempts to determine tax benefits that are more likely to be used by potential fraud taxpayers by means of investigating the Personal Income Tax structure. Secondly, it aims at characterizing through socioeconomic variables the segment profiles of potential fraud taxpayer to offer an audit selection strategy for improving tax compliance and improve tax design. An application to the annual Spanish Personal Income Tax sample designed by the Institute for Fiscal Studies is provided. Results obtained confirm that the combination of data mining techniques proposed offers valuable information to contribute to the study of tax frau
GraphFC: Customs Fraud Detection with Label Scarcity
Custom officials across the world encounter huge volumes of transactions.
With increased connectivity and globalization, the customs transactions
continue to grow every year. Associated with customs transactions is the
customs fraud - the intentional manipulation of goods declarations to avoid the
taxes and duties. With limited manpower, the custom offices can only undertake
manual inspection of a limited number of declarations. This necessitates the
need for automating the customs fraud detection by machine learning (ML)
techniques. Due the limited manual inspection for labeling the new-incoming
declarations, the ML approach should have robust performance subject to the
scarcity of labeled data. However, current approaches for customs fraud
detection are not well suited and designed for this real-world setting. In this
work, we propose ( neural networks for
ustoms raud), a model-agnostic, domain-specific,
semi-supervised graph neural network based customs fraud detection algorithm
that has strong semi-supervised and inductive capabilities. With upto 252%
relative increase in recall over the present state-of-the-art, extensive
experimentation on real customs data from customs administrations of three
different countries demonstrate that GraphFC consistently outperforms various
baselines and the present state-of-art by a large margin
A Comprehensive Survey of Data Mining-based Fraud Detection Research
This survey paper categorises, compares, and summarises from almost all
published technical and review articles in automated fraud detection within the
last 10 years. It defines the professional fraudster, formalises the main types
and subtypes of known fraud, and presents the nature of data evidence collected
within affected industries. Within the business context of mining the data to
achieve higher cost savings, this research presents methods and techniques
together with their problems. Compared to all related reviews on fraud
detection, this survey covers much more technical articles and is the only one,
to the best of our knowledge, which proposes alternative data and solutions
from related domains.Comment: 14 page
Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty
Risk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME) based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities. In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications
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