000			01941nam a2200205 4500
999			_c383052 _d383052
003			APU
005			20200312010353.0
008			200311b2019 xxu||||| |||| 00| 0 eng d
050			_aPG-24-0110
100	0		_aWONG YUN SENG (TP038263) _945620
245	1	4	_aTHE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) MODEL AS A FINANCIAL STATEMENT FRAUD (FSF) PREDITOR / _cWONG YUN SENG.
260			_aKuala Lumpur : _bAsia Pacific University, _c2019.
300			_a78 pages : _billustrations ; _c30 cm.
502			_aA 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			_aThis 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	_aFraud _xPrevention. _945612
650		0	_aNeural networks (Computer science) _945621
700	0		_aMs. Geetha Rubasundram _eSupervisor. _945623
942			_2lcc _cUndergraduate Theses