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ABSTRACT
Title |
: |
KNOWLEDGE STRUCTURE FOR FRAUDULENT FIRM CLASSIFICATION APPROACHES |
Authors |
: |
Dr.G.Ayyappan, Dr.A.Kumaravel |
Keywords |
: |
J48, Fraudulent Firm, Audit data, Machine Learning |
Issue Date |
: |
Aug-Sep 2019 |
Abstract |
: |
Machine learning gives most impact in developing the qualities of an audit work in the future due to enormous development of financial fraud. Here, seven hundred and seventy seven firm’s data in fourteen different sectors are collected in this research study. This work proposed number of classification models are estimated in terms of their accuracies and time taken to build the models. The results of Trees and Rules are demonstrating an accuracy of high level for suspicious firm classification. This paper focuses improving the worth of an audit work by implementing a machine learning algorithms. |
Page(s) |
: |
89-96 |
ISSN |
: |
0976-5166 |
Source |
: |
Vol. 10, No.4 |
PDF |
: |
Download |
DOI |
: |
10.21817/indjcse/2019/v10i4/191004008 |
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