scholarly journals Responsibility of an auditor for accounting fraud detection

Skola biznisa ◽  
2016 ◽  
pp. 89-101 ◽  
Author(s):  
Sanja Vlaović-Begović ◽  
Stevan Tomašević
Keyword(s):  
Fraud Detection ◽  
Accounting Fraud

scholarly journals An Ensemble Model for Financial Statement Fraud Detection

2021 ◽  
Author(s):  
Ahmed M. Khedr ◽  
Magdi El Bannany ◽  
Sakeena Kanakkayil
Keyword(s):  
Class Imbalance ◽  
Fraud Detection ◽  
Financial Statements ◽  
Global Market ◽  
Financial Statement ◽  
Machine Learning Techniques ◽  
Accounting Fraud ◽  
Financial Statement Fraud ◽  
Mena Region ◽  
Ensemble Technique

Fraudulent financial statements are deliberate furnishing and/or reporting incorrect statistics, and this has become a major economic and social concern as the global market is witnessing an upsurge in financial accounting fraud, costing businesses billions of dollars a year. Identifying companies that manipulate financial statements remains a challenge for auditors, as fraud strategies have become increasingly sophisticated over the years. We evaluate machine learning techniques for financial statement fraud detection, particularly a powerful ensemble technique, the XGBoost algorithm, that help to identify fraud on a set of sample companies drawn from the MENA region. The issue of the class imbalance in the dataset is addressed by applying the SMOTE algorithm. We found that XGBoost algorithm outperformed other algorithms in this study: Logistic Regression (LR), Decision Tree (DT), Vector Machine Support (SVM), Adaboost, and RandomForest. The XGBoost algorithm is then optimised to obtain the optimum performance.

scholarly journals THE USE OF MACHINE LEARNING TECHNIQUES IN ACCOUNTING. A SHORT SURVEY

2021 ◽  
Vol 4 (3) ◽  
pp. 139-143
Author(s):  
Mariana Vlad ◽  
◽  
Sorin Vlad ◽  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Real Life ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Behavior Prediction ◽  
Accounting Fraud ◽  
Research Fields ◽  
Learning Techniques

Machine learning (ML) is a subset of artificial Intelligence (AI) aiming to develop systems that can learn and continuously improve the abilities through generalization in an autonomous manner. ML is presently all around us, almost every facet of our digital and real life is embedding some ML related content. Customer recommendation systems, customer behavior prediction, fraud detection, speech recognition, image recognition, black & white movies colorization, accounting fraud detection are just some examples of the vast range of applications in which ML is involved. The techniques that this paper investigates are mainly focused on the use of neural networks in accounting and finance research fields. An artificial neural network is modelling the brain ability of learning intricate patterns from the information presented at its inputs using elementary interconnected units, named neurons, grouped in layers and trained by means of a learning algorithm. The performance of the network depends on many factors like the number of layers, the number of each neurons in each layer, the learning algorithm, activation functions, to name just a few of them. Machine learning algorithms have already started to replace humans in jobs that require document’s processing and decision making.

scholarly journals PREDICCIÓN DE FRAUDES EN EL CONSUMO DE AGUA POTABLE MEDIANTE EL USO DE MINERÍA DE DATOS

2020 ◽  
Vol 24 (104) ◽  
pp. 58-66
Author(s):  
Fredy Humberto Troncoso Espinosa ◽  
Fuentes Figueroa Paulina Gisselot ◽  
Italo Ramiro Belmar Arriagada
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
New York ◽  
Electrical Power ◽  
Fraud Detection ◽  
Accounting Fraud ◽  
Power Utility ◽  
Palabras Clave ◽  
Comprehensive Survey ◽  
Prevention Methods

El comportamiento fraudulento en el consumo de agua potable es un problema importante que enfrentan las empresas de tratamiento de agua debido a que genera pérdidas económicas significativas. Caracterizar consumos fraudulentos es una tarea compleja, basada principalmente en la experiencia, y que presenta el desafío de la incorporación constante de nuevos clientes y la variación en el consumo mensual. En esta investigación, las técnicas de minería de datos se utilizan para caracterizar y predecir los consumos fraudulentos de agua potable. Para esto, se utilizó información histórica relacionada con el consumo. Las técnicas aplicadas mostraron un alto rendimiento predictivo y su aplicación permitirá enfocar eficientemente los recursos orientados a evitar este tipo de fraude. Palabras Clave: minería de datos, machine learning, agua potable, detección de fraude. Referencias [1]Centro de Investigación Periodística., «Producción y facturación de agua potable,» 30 Julio 2020. [En línea]. Disponible en: https://ciperchile.cl/wp-content/uploads/gestion-siis-2014-pag 88.pdf. [Último acceso: 30 Julio 2020]. [2]Bureau Veritas S.A., «https://www.bureauveritas.cl/es,» [En línea]. Disponible en: https://www.bureauveritas.cl/es/bureau-veritas-lider-mundial-en-ensayos-inspeccion-y-certificacion. [Último acceso: 1 Junio 2020]. [3]Essbio S.A., «www.essbio.cl,» [En línea]. [4]I. Monedero, F. Biscarri, J. Guerrero, M. Peña, M. Roldán y C. León, «Detection of water meter under-registration using statistical algorithms,» Journal of Water Resources Planning and Management, vol. 142, nº 1, p. 04015036, 2016. [5]I. Monedero, F. Biscarri, C. León, J. Guerrero, J. Biscarri y R. Millán, «Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees,» International Journal of Electrical Power & Energy Systems, vol. 34, nº 1, pp. 90-98, 2012. [6]S. Wang, «A comprehensive survey of data mining-based accounting-fraud detection research,» de 2010 International Conference on Intelligent Computation Technology and Automation, New York, 2010. [7]J. Bierstaker, R. Brody y C. Pacini, «Accountants' perceptions regarding fraud detection and prevention methods,» Managerial Auditing Journal, vol. 21, nº 5, pp. 520-535, 2006. [8]C. Phua, V. Lee, K. Smith y R. Gayler, «A comprehensive survey of data mining-based fraud detection research,» arXiv preprint arXiv:1009.6119, 2010. [9]S. Kotsiantis, I. Zaharakis y P. Pintelas, «Machine learning: a review of classification and combining techniques,» Artificial Intelligence Review, vol. 26, nº 3, pp. 159-190, 2006. [10]J. Han, J. Pei y M. Kamber, Data Mining: Concepts and Techniques, Elsevier, 2011.  

scholarly journals FORENSIC ACCOUNTING: FRAUD DETECTION SKILLS FOR EXTERNAL AUDITORS

2019 ◽  
Vol 20 (1) ◽  
pp. 168-180
Author(s):  
Sri Fadilah ◽  
Mey Maemunah ◽  
Nurrahmawati Nurrahmawati ◽  
Tomy Nusa Lim ◽  
Rini Irianti Sundary
Keyword(s):  
Fraud Detection ◽  
Accounting Fraud ◽  
External Auditors ◽  
Forensic Accounting

A Study on Various Applications of Data Mining and Supervised Learning Techniques in Business Fraud Detection

2021 ◽  
pp. 108-125
Author(s):  
Amit Majumder ◽  
Ira Nath
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supervised Learning ◽  
Academic Research ◽  
Fraud Detection ◽  
Customer Relationships ◽  
Financial Accounting ◽  
Accounting Fraud ◽  
Data Mining Technique ◽  
Learning Techniques

Data mining technique helps us to extract useful data from a large dataset of any raw data. It is used to analyse and identify data patterns and to find anomalies and correlations within dataset to predict outcomes. Using a broad range of techniques, we can use this information to improve customer relationships and reduce risks. Data mining and supervised learning have applications in multiple fields of science and research. Machine learning looks at patterns of data and helps to predict future behaviour by learning from the patterns. Data mining is normally used as a source of information on which machine learning can be applied to solve some of problems in our daily life. Supervised learning is one type of machine learning method which uses labelled data consisting of input along with the label of inputs and generates one learned model (or classifier for classification type work) which can be used to label unknown data. Financial accounting fraud detection has become an emerging topic in the field of academic, research and industries.

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