Shivshankar Reddy
ABSTRACT
Detection of fraudulent transactions is an imperative research area in the financial domain, affecting the different entities involved in the payment process. An accurate fraud detection algorithm will help in identifying fraudulent transactions, thus facilitating immediate response and dispute resolution. To this effect, this research proposes a novel framework TeGraF for detecting fraudulent transactions by modeling temporal and structural features from a given input. The proposed algorithm operates at the intersection of two key research areas: Temporal Point Processes (TPPs) and Graph Neural Networks (GNNs). Due to the wide occurrence of sequential data in the financial domain, TPPs are very useful for modeling the sequence of transactions. Parallelly, the financial domain data can also be represented as a graphical structure capturing interactions between users and vendors/merchants. Thus, the proposed algorithm utilizes the temporal features learned via the TPP based model and the structural features captured via the GNN for modeling fraudulent transactions. Different graph representation learning techniques like Node2Vec, Metapath2Vec, LINE, DeepWalk, and BiNE are employed to compare the overall performance. Experiments have been evaluated on a synthetic dataset containing 62K users and 4M transactions, which demonstrate the improved performance of the proposed technique as compared to the existing algorithms.
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Index Terms
- TeGraF: temporal and graph based fraudulent transaction detection framework
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