THE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) MODEL AS A FINANCIAL STATEMENT FRAUD (FSF) PREDITOR / (Record no. 383052)
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000 -LEADER | |
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fixed length control field | 01941nam a2200205 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | APU |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200312010353.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 200311b2019 xxu||||| |||| 00| 0 eng d |
050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | PG-24-0110 |
100 0# - MAIN ENTRY--PERSONAL NAME | |
Personal name | WONG YUN SENG (TP038263) |
9 (RLIN) | 45620 |
245 14 - TITLE STATEMENT | |
Title | THE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) MODEL AS A FINANCIAL STATEMENT FRAUD (FSF) PREDITOR / |
Statement of responsibility, etc | WONG YUN SENG. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc | Kuala Lumpur : |
Name of publisher, distributor, etc | Asia Pacific University, |
Date of publication, distribution, etc | 2019. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | 78 pages : |
Other physical details | illustrations ; |
Dimensions | 30 cm. |
502 ## - DISSERTATION NOTE | |
Dissertation note | A project submitted in partial fulfillment of the requirements of Asia Pacific University of Technology and Innovation for the degree of BSc (Hons) in Accounting and Finance with specialism in Forensic Accounting (UC3F1810AF(FA)). |
520 ## - SUMMARY, ETC. | |
Summary, etc | This study applies the Artificial Neural Network (ANN) to predict Financial Statement Fraud (FSF). FSF is a type of fraudulent activity, it involves the misinterpretation of the financial statements intentionally. Financial statements that conceal the company's true financial situation causes a huge loss to market participants, including creditors, employees, shareholders, and investors. Previous research has found that ANN is one of the efficient ways to predict FSF. This study will introduce the technological and methodological characteristics of ANN model using the Multilayer Feed Forward Neural Network (MLF) model to predict FSF. The MLF was adapted to enable pattern recognition, function fitting, pattern approximation, classification and to overcome prediction problems. This research had focused on fifty fraudulent firms and five-year time series to predict FSF in Malaysia. The findings of this research show that the ANN model is able to predict FSF accurately at 84.2%. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Fraud |
General subdivision | Prevention. |
9 (RLIN) | 45612 |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Neural networks (Computer science) |
9 (RLIN) | 45621 |
700 0# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Ms. Geetha Rubasundram |
Relator term | Supervisor. |
-- | 45623 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | |
Koha item type | Undergraduate Theses |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Use restrictions | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Copy number | Koha item type |
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Not Withdrawn | Available | Not Damaged | Restricted access | Not for loan | Undergraduate Theses | APU Library | APU Library | Reference Collection | 11/03/2020 | PG-24-0110 | 00017744 | 11/03/2020 | 1 | Reference |