Artificial Neural Network (ANN): An Artificial Intelligent (AI) Tool to Predict Fraudulent Financial Reporting and Financial Distress

Authors

  • Vatsala Vijayan Research Scholar, Asia Pacific University of Technology & Innovation, Jalan Teknologi 5, 57000 Wilayah Persekutuan Kuala Lumpur, Malaysia Author
  • Geetha A. Rubasundram Senior Lecturer, Asia Pacific University of Technology & Innovation, Jalan Teknologi 5, 57000 Wilayah Persekutuan Kuala Lumpur, Malaysia Author

DOI:

https://doi.org/10.61841/mv02ey11

Keywords:

Artificial Neural Network (ANN), Artificial Intelligence (AI), Fraudulent Financial Reporting (FFR), Financial Distress (FD), Financial Ratios

Abstract

Artificial Neural Network (ANN) is an artificial intelligence (AI) tool to predict fraudulent financial reporting and financial distress. This paper explores the effectiveness of the AI tool in accomplishing the task of management fraud detection; auditors could be facilitated in their work by using the Artificial Neural Network technique. The input vector of the ANN study is composed of financial ratios from the firm’s financial statements, such as the working capital, total assets, total liability, inventories, and cost of sales, sales, and net income. Based on Bursa Malaysia, the sample data taken is based on PN17 companies, which means the companies that are currently facing FD and companies that committed financial fraud. This research employs seven proxy variables from 240 observations for quantitative analysis and also investigates the usefulness of neural networks in predicting fraudulent financial reporting and financial distress. The results reflect that the ANN model used is accurate. 

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Published

30.04.2020

Issue

Vol. 24 No. 2 (2020): 24 (2) 2020

Section

Articles

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How to Cite

Vijayan, V., & A. Rubasundram, G. (2020). Artificial Neural Network (ANN): An Artificial Intelligent (AI) Tool to Predict Fraudulent Financial Reporting and Financial Distress. International Journal of Psychosocial Rehabilitation, 24(2), 1215-1226. https://doi.org/10.61841/mv02ey11