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008			250429t20232023my a|||fs|||| 000 0 eng d
020			_aTHE0010014 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .L46 2023 r Bc.
100	1		_aLeong, Teng Man, _eauthor.
245	1	0	_aTemporal graph for fraud detection and analytics / _cLeong Teng Man
264		1	_aKuantan, Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_axi, 39 pages : _billustrations ; _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCentre for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 20223
504			_aIncludes bibliographical references
520	3		_aRepresenting time-based transactions as graphs offers insights for fraud detection. However, limited and confidential datasets pose challenges. To address this, the project aims to assess the quality of generative model by using the generated data in training the fraud classifier model. A generative approach using graph autoencoders is employed to augment data for fraud detection. Experiments utilize the Elliptic dataset, a Bitcoin transaction dataset with 49 connected component graphs representing confirmations at twoweek intervals. A custom variational graph autoencoder (VGAE) is developed, encountering challenges with the defined loss function, resulting in excessive edge reconstruction. Among experimented three graph neural network (GNN) models—Graph Convolutional Networks (GCN), Graph Attention Networks (GAT) and GATv2, it is found that none effectively detect unseen illicit transactions. While GAT and GATv2 outperform GCN, models trained on reconstructed data perform worse than those on purely original data. It is observed the consistent metric fluctuations among models regardless of data used during training. This indicates the VGAE captures underlying patterns. This study highlights challenges and suggests future research directions, including adversarially regularized graph autoencoders, alternative architectures, efficient loss functions, and ensemble methods.
610	2	0	_xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Project _xDissertations
942			_2lcc _cPSM