Asia Pacific University Library catalogue


THE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) MODEL AS A FINANCIAL STATEMENT FRAUD (FSF) PREDITOR / (Record no. 383052)

000 -LEADER
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
Holdings
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
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