Artificial Neural Network Models for Predicting Patterns in Auditing Monthly Balances

Koskivaara, Eija

doi:10.2307/254227

Cited by 6 publications

(7 citation statements)

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“…Coakley et al (1993) seed material errors in the monthly data and applied the artificial neural network to the ratio analysis for error detections. Koskivaara (2000) compares the analytical procedure using regression and the integrated analytical procedure using the Artificial Neural Network (ANN) which is embedded in the continuous auditing environment. In addition to error and fraud detection, neural networks have also been used in making control risk assessment.…”

Section: Continuous Auditingmentioning

confidence: 99%

Cluster Analysis for Anomaly Detection in Accounting*

Thiprungsri¹

2019

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry

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Section: Continuous Auditingmentioning

confidence: 99%

Cluster Analysis for Anomaly Detection in Accounting*

Thiprungsri¹

2019

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry

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“…It receives intelligence through learning procedure. In other words, the network is trained to perform certain tasks with a given set of examples (Koskivaara, 2000). A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting fraudulent financial reporting using artificial neural network

Omar¹,

Johari

Smith

2017

JFC

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Purpose This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia. Design/methodology/approach Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN. Findings The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting. Originality/value The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.

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“…The applications can be found in, just to name a few, stock index forecasting (Rhee, 1994;Qi, 1999;Qi and Maddala, 1999), bankruptcy forecasting (Altman et al, 1994;Wilson and Sharda, 1994;Sharda and Wilson, 1996;Kerling, 1996;Lee et al, 1996;Jo et al, 1997;Davalos et al, 1999;Atiya, 2001), bonds rating (Dutta and Shekhar, 1988;Moody and Utans, 1991;Surkan and Singleton, 1991;Kim et al, 1993), mortgage risk assessing (Collins et al, 1988;Reilly et al, 1991;Grudnitski et al, 1995) and auditing (Anandarajan and Anandarajan, 1999;Koh and Tan, 1999;Etheridge et al, 2000;Feroz et al, 2000;Koskivaara, 2000;Calderon and Cheh, 2002).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?

Lin

Khan

Chang

et al. 2008

Journal of International Money and Finance

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This paper presents a hybrid causal model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of the neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to neural network and the traditionally used techniques such as logic, the proposed model can thus lead to a somewhat more prescriptive modeling approach based on determinate causal mechanisms towards finding ways to prevent currency crises. (C) 2008 Elsevier Ltd. All rights reserved

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Artificial Neural Network Models for Predicting Patterns in Auditing Monthly Balances

Cited by 6 publications

References 0 publications

Cluster Analysis for Anomaly Detection in Accounting*

Cluster Analysis for Anomaly Detection in Accounting*

Predicting fraudulent financial reporting using artificial neural network

A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?

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