5,013 research outputs found
FraudMemory: Explainable Memory-Enhanced Sequential Neural Networks for Financial Fraud Detection
The rapid development of electronic financial services brings significant convenience to our daily life. However, it also offers criminals the opportunity to exploit financial systems to do fraudulent transactions. Previous studies on fraud detection only deal with single type transactions and cannot adapt well to evolving environment in reality. In addition, their black box models pay less attention on the interpretability of fraud detection results. Here we propose a novel fraud detection algorithm called FraudMemory. It adopts state-of-art feature representation methods to better depict users and logs with multiple types in financial systems. Our model innovatively uses sequential model to capture the sequential patterns of each transaction and leverages memory networks to improve both the performance and interpretability. Also, with the incorporation of memory components, FraudMemory possesses high adaptability to the existence of concept drift. The empirical study proves that our model is a potential tool for financial fraud detection
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
The recent advent of play-to-earn (P2E) systems in massively multiplayer
online role-playing games (MMORPGs) has made in-game goods interchangeable with
real-world values more than ever before. The goods in the P2E MMORPGs can be
directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn
via blockchain networks. Unlike traditional in-game goods, once they had been
written to the blockchains, P2E goods cannot be restored by the game operation
teams even with chargeback fraud such as payment fraud, cancellation, or
refund. To tackle the problem, we propose a novel chargeback fraud prediction
method, PU GNN, which leverages graph attention networks with PU loss to
capture both the players' in-game behavior with P2E token transaction patterns.
With the adoption of modified GraphSMOTE, the proposed model handles the
imbalanced distribution of labels in chargeback fraud datasets. The conducted
experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN
achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac
Detecting credit card fraud: An analysis of fraud detection techniques
Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced machine learning techniques and other supportive algorithms to detect and prevent fraud in the future. This work analyzes the various machine learning techniques for their ability to efficiently detect fraud and explores additional state-of-the-art techniques to assist with their performance. This work also proposes a generalized strategy to detect fraud regardless of a dataset\u27s features or unique characteristics. The high performing models discovered through this generalized strategy lay the foundation to build additional models based on state-of-the-art methods. This work expands on the issues of fraud detection, such as missing data and unbalanced datasets, and highlights models that combat these issues. Furthermore, state-of-the-art techniques, such as adapting to concept drift, are employed to combat fraud adaptation
Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer
How to obtain informative representations of transactions and then perform
the identification of fraudulent transactions is a crucial part of ensuring
financial security. Recent studies apply Graph Neural Networks (GNNs) to the
transaction fraud detection problem. Nevertheless, they encounter challenges in
effectively learning spatial-temporal information due to structural
limitations. Moreover, few prior GNN-based detectors have recognized the
significance of incorporating global information, which encompasses similar
behavioral patterns and offers valuable insights for discriminative
representation learning. Therefore, we propose a novel heterogeneous graph
neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for
transaction fraud detection problems. Specifically, we design a temporal
encoding strategy to capture temporal dependencies and incorporate it into the
graph neural network framework, enhancing spatial-temporal information modeling
and improving expressive ability. Furthermore, we introduce a transformer
module to learn local and global information. Pairwise node-node interactions
overcome the limitation of the GNN structure and build up the interactions with
the target node and long-distance ones. Experimental results on two financial
datasets compared to general GNN models and GNN-based fraud detectors
demonstrate that our proposed method STA-GT is effective on the transaction
fraud detection task
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of
transactions to improve customer experience and minimize financial loss. In
this work, we propose xFraud, an explainable fraud transaction prediction
framework which is mainly composed of a detector and an explainer. The xFraud
detector can effectively and efficiently predict the legitimacy of incoming
transactions. Specifically, it utilizes a heterogeneous graph neural network to
learn expressive representations from the informative heterogeneously typed
entities in the transaction logs. The explainer in xFraud can generate
meaningful and human-understandable explanations from graphs to facilitate
further processes in the business unit. In our experiments with xFraud on real
transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud
is able to outperform various baseline models in many evaluation metrics while
remaining scalable in distributed settings. In addition, we show that xFraud
explainer can generate reasonable explanations to significantly assist the
business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15
(3) (VLDB 2022
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