A Semi-supervised Graph Attentive Network for Financial Fraud Detection
- URL: http://arxiv.org/abs/2003.01171v1
- Date: Fri, 28 Feb 2020 10:35:25 GMT
- Title: A Semi-supervised Graph Attentive Network for Financial Fraud Detection
- Authors: Daixin Wang and Jianbin Lin and Peng Cui and Quanhui Jia and Zhen Wang
and Yanming Fang and Quan Yu and Jun Zhou and Shuang Yang and Yuan Qi
- Abstract summary: We propose a semi-supervised attentive graph neural network, namedSemiSemiGNN, to utilize the multi-view labeled and unlabeled data for fraud detection.
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.
- Score: 30.645390612737266
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection [0.7864304771129751]
This paper proposes a novel approach for credit card fraud detection using Graph Neural Networks (GNNs) with attention mechanisms applied to heterogeneous graph representations of financial data.
The proposed model outperforms benchmark algorithms such as Graph Sage and FI-GRL, achieving a superior AUC-PR of 0.89 and an F1-score of 0.81.
arXiv Detail & Related papers (2024-10-10T17:05:27Z) - Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks [39.54354926067617]
Graph neural networks are a type of deep learning model that can utilize the interactive relationships within graph structures.
fraudulent activities only account for a very small part of transaction transfers.
fraudsters often disguise their behavior, which can have a negative impact on the final prediction results.
arXiv Detail & Related papers (2024-09-15T23:08:31Z) - Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation [17.314619091307343]
We describe a privacy-preserving framework that allows financial institutions to jointly train highly accurate anomaly detection models.
We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data.
arXiv Detail & Related papers (2023-10-06T19:16:41Z) - Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection.
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
arXiv Detail & Related papers (2023-07-11T07:48:39Z) - LaundroGraph: Self-Supervised Graph Representation Learning for
Anti-Money Laundering [5.478764356647437]
LaundroGraph is a novel self-supervised graph representation learning approach.
It provides insights to assist the anti-money laundering reviewing process.
To the best of our knowledge, this is the first fully self-supervised system within the context of AML detection.
arXiv Detail & Related papers (2022-10-25T21:58:02Z) - Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
Graph Neural Networks (GNNs) have shown solid performance on fraud detection.
labeled data is scarce in large-scale industrial problems, especially for fraud detection.
We propose a novel graph pre-training strategy to leverage more unlabeled data.
arXiv Detail & Related papers (2021-10-04T03:42:09Z) - Relational Graph Neural Networks for Fraud Detection in a Super-App
environment [53.561797148529664]
We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App.
We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users.
Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
arXiv Detail & Related papers (2021-07-29T00:02:06Z) - Supporting Financial Inclusion with Graph Machine Learning and Super-App
Alternative Data [63.942632088208505]
Super-Apps have changed the way we think about the interactions between users and commerce.
This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
arXiv Detail & Related papers (2021-02-19T15:13:06Z) - DFraud3- Multi-Component Fraud Detection freeof Cold-start [50.779498955162644]
The Cold-start is a significant problem referring to the failure of a detection system to recognize the authenticity of a new user.
In this paper, we model a review system as a Heterogeneous InformationNetwork (HIN) which enables a unique representation to every component.
HIN with graph induction helps to address the camouflage issue (fraudsterswith genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews.
arXiv Detail & Related papers (2020-06-10T08:20:13Z) - Leveraging Multi-Source Weak Social Supervision for Early Detection of
Fake News [67.53424807783414]
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
This unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation.
We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances.
Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.
arXiv Detail & Related papers (2020-04-03T18:26:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.