768 research outputs found
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
With the rapid growth of financial services, fraud detection has been a very
important problem to guarantee a healthy environment for both users and
providers. Conventional solutions for fraud detection mainly use some
rule-based methods or distract some features manually to perform prediction.
However, in financial services, users have rich interactions and they
themselves always show multifaceted information. These data form a large
multiview network, which is not fully exploited by conventional methods.
Additionally, among the network, only very few of the users are labelled, which
also poses a great challenge for only utilizing labeled data to achieve a
satisfied performance on fraud detection.
To address the problem, we expand the labeled data through their social
relations to get the unlabeled data and propose a semi-supervised attentive
graph neural network, namedSemiGNN to utilize the multi-view labeled and
unlabeled data for fraud detection. Moreover, we propose a hierarchical
attention mechanism to better correlate different neighbors and different
views. Simultaneously, the attention mechanism can make the model interpretable
and tell what are the important factors for the fraud and why the users are
predicted as fraud. Experimentally, we conduct the prediction task on the users
of Alipay, one of the largest third-party online and offline cashless payment
platform serving more than 4 hundreds of million users in China. By utilizing
the social relations and the user attributes, our method can achieve a better
accuracy compared with the state-of-the-art methods on two tasks. Moreover, the
interpretable results also give interesting intuitions regarding the tasks.Comment: icd
GraphFC: Customs Fraud Detection with Label Scarcity
Custom officials across the world encounter huge volumes of transactions.
With increased connectivity and globalization, the customs transactions
continue to grow every year. Associated with customs transactions is the
customs fraud - the intentional manipulation of goods declarations to avoid the
taxes and duties. With limited manpower, the custom offices can only undertake
manual inspection of a limited number of declarations. This necessitates the
need for automating the customs fraud detection by machine learning (ML)
techniques. Due the limited manual inspection for labeling the new-incoming
declarations, the ML approach should have robust performance subject to the
scarcity of labeled data. However, current approaches for customs fraud
detection are not well suited and designed for this real-world setting. In this
work, we propose ( neural networks for
ustoms raud), a model-agnostic, domain-specific,
semi-supervised graph neural network based customs fraud detection algorithm
that has strong semi-supervised and inductive capabilities. With upto 252%
relative increase in recall over the present state-of-the-art, extensive
experimentation on real customs data from customs administrations of three
different countries demonstrate that GraphFC consistently outperforms various
baselines and the present state-of-art by a large margin
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
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Graph Neural Networks (GNNs) have been widely applied to fraud detection
problems in recent years, revealing the suspiciousness of nodes by aggregating
their neighborhood information via different relations. However, few prior
works have noticed the camouflage behavior of fraudsters, which could hamper
the performance of GNN-based fraud detectors during the aggregation process. In
this paper, we introduce two types of camouflages based on recent empirical
studies, i.e., the feature camouflage and the relation camouflage. Existing
GNNs have not addressed these two camouflages, which results in their poor
performance in fraud detection problems. Alternatively, we propose a new model
named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation
process with three unique modules against camouflages. Concretely, we first
devise a label-aware similarity measure to find informative neighboring nodes.
Then, we leverage reinforcement learning (RL) to find the optimal amounts of
neighbors to be selected. Finally, the selected neighbors across different
relations are aggregated together. Comprehensive experiments on two real-world
fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed
CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors.
We integrate all GNN-based fraud detectors as an opensource toolbox:
https://github.com/safe-graph/DGFraud. The CARE-GNN code and datasets are
available at https://github.com/YingtongDou/CARE-GNN.Comment: Accepted by CIKM 202
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Graphs represent interconnected structures prevalent in a myriad of
real-world scenarios. Effective graph analytics, such as graph learning
methods, enables users to gain profound insights from graph data, underpinning
various tasks including node classification and link prediction. However, these
methods often suffer from data imbalance, a common issue in graph data where
certain segments possess abundant data while others are scarce, thereby leading
to biased learning outcomes. This necessitates the emerging field of imbalanced
learning on graphs, which aims to correct these data distribution skews for
more accurate and representative learning outcomes. In this survey, we embark
on a comprehensive review of the literature on imbalanced learning on graphs.
We begin by providing a definitive understanding of the concept and related
terminologies, establishing a strong foundational understanding for readers.
Following this, we propose two comprehensive taxonomies: (1) the problem
taxonomy, which describes the forms of imbalance we consider, the associated
tasks, and potential solutions; (2) the technique taxonomy, which details key
strategies for addressing these imbalances, and aids readers in their method
selection process. Finally, we suggest prospective future directions for both
problems and techniques within the sphere of imbalanced learning on graphs,
fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on
graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG
Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network
Financial frauds cause billions of losses annually and yet it lacks efficient
approaches in detecting frauds considering user profile and their behaviors
simultaneously in social network . A social network forms a graph structure
whilst Graph neural networks (GNN), a promising research domain in Deep
Learning, can seamlessly process non-Euclidean graph data . In financial fraud
detection, the modus operandi of criminals can be identified by analyzing user
profile and their behaviors such as transaction, loaning etc. as well as their
social connectivity. Currently, most GNNs are incapable of selecting important
neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored.
In this paper, we propose a novel behavior information aggregation network
(BIAN) to combine the user behaviors with other user features. Different from
its close "relatives" such as Graph Attention Networks (GAT) and Graph
Transformer Networks (GTN), it aggregates neighbors based on neighboring edge
attribute distribution, namely, user behaviors in financial social network. The
experimental results on a real-world large-scale financial social network
dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with
the State-Of-The-Art models.Comment: 6 pages, 1 figur
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