In recent years, there has been a rapid increase in the number of online transactions. Substantial growth has been reported in e-commerce and e-governance in the past few years. Due to this the number of people using online payment methods has also increased. This has led to an exponential rise in the number of transactions that happen every day. This increase in online transactions has further led to an increase in the number of frauds in the transactions. There is an ever-growing need to detect these fraudulent transactions as early as possible so that appropriate actions could be taken and losses due to these frauds could be minimized. This work proposes machine learning models which could use the previously known data and try to predict frauds based on information learned through the old data. We propose a statistical based dimensionality reduction technique and various machine learning models were tried for classification purpose. We experimented our proposed method on IEEE-CIS Fraud Detection dataset and the best results were obtained on the XGBoost model which is demonstrated in this paper.
As e-commerce has expanded, people's lives now include some aspect of online buying, because buyers frequently use online product reviews to make purchasing decisions. Merchants frequently collaborate with review spammers to write spam reviews that promote or demote selected items. Spammers who work in groups, in particular, are more dangerous than individual attacks. Previous studies provided various frequent item mining and graphbased techniques to detect such spammer groups. In this paper, we recommend a technique referred to as GrFrauder (Group Fraud detection) method to detect online spam reviewer groups with an unsupervised manner. Our technology identifies spammer candidate groups initially based on product -product review graph and collaboration among reviewers constructed with several behavioral patterns. It then embeds reviewers into an embedding space and calculates spam score for every group; with higher spam scores the model generates ranks for each group. Studies using four real-world datasets reveal that GrFrauder outperforms numerous state-of-the-art baselines in terms of precision and is able to identify more high-quality spammer groups.
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