A framework for internal fraud risk reduction at it integrating business processes : the IFR² framework

Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call ‘internal fraud risk reduction’. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.El fraude es un negocio millonario y está aumentando cada año. Tanto el fraude interno como el externo presentan un coste considerable para nuestra economía en todo el mundo. Este artículo sobre la literatura académica enseña que la comunidad académica solo se dirige al fraude externo, y cómo se detecta este tipo de fraude. Que sepamos, se ha hecho poco o ningún esfuerzo en investigar cómo evitar y detectar el fraude interno, al que llamamos ‘reducción del riesgo de fraude interno’. Teniendo en cuenta la urgencia de investigar el fraude interno, y la ausencia de ello en la literatura académica, la investigación para reducir este tipo de fraude es esencial. Este tema puede ser aún investigado con mayor profundidad solo después de tener un marco, en el que implementar investigación empírica. En este artículo, presentamos el marco IFR, deducido tanto de la literatura académica como de las prácticas empresariales actuales, donde el foco del marco sugiere usar un enfoque de extracción de datos

Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels

To produce important investment decisions, investors require financial records and economic information. However, most companies manipulate investors and financial institutions by inflating their financial statements. Fraudulent Financial Activities exist in any monetary or financial transaction scenario, whether physical or electronic. A challenging problem that arises in this domain is the issue that affects and troubles individuals and institutions. This problem has attracted more attention in the field in part owing to the prevalence of financial fraud and the paucity of previous research. For this purpose, in this study, the main approach to solve this problem, an anomaly detection-based approach based on a combination of feature selection based on squirrel optimization pattern and classification methods have been used. The aim is to develop this method to provide a model for detecting anomalies in financial statements using a combination of selected features with the nearest neighbor classifications, neural networks, support vector machine, and Bayesian. Anomaly samples are then analyzed and compared to recommended techniques using assessment criteria. Squirrel optimization's meta-exploratory capability, along with the approach's ability to identify abnormalities in financial data, has been shown to be effective in implementing the suggested strategy. They discovered fake financial statements because of their expertise

Data Mining Techniques for Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri

Data Mining Techniques in Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models

Systemic acquired critique of credit card deception exposure through machine learning

Artigo publicado em revista científica internacionalA wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.info:eu-repo/semantics/publishedVersio

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