Awesome

AntiFraud

A Financial Fraud Detection Framework.

Source codes implementation of papers:

• MCNN: Credit card fraud detection using convolutional neural networks, in ICONIP 2016.
• STAN: Spatio-temporal attention-based neural network for credit card fraud detection, in AAAI2020
• STAGN: Graph Neural Network for Fraud Detection via Spatial-temporal Attention, in TKDE2020
• GTAN: Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation, in AAAI2023
• RGTAN: Enhancing Attribute-driven Fraud Detection with Risk-aware Graph Representation,
• HOGRL: Effective High-order Graph Representation Learning for Credit Card Fraud Detection,

Usage

Data processing

1. Run unzip /data/Amazon.zip and unzip /data/YelpChi.zip to unzip the datasets;
2. Run python feature_engineering/data_process.py to pre-process all datasets needed in this repo.
3. Run python feature_engineering/get_matrix.py to generate the adjacency matrix of the high-order transaction graph.Please note that this will require approximately 280GB of storage space. Please be aware that if you intend to run HOGRL , you should first execute the get_matrix.py script.

Training & Evalutaion

To test implementations of MCNN, STAN and STAGN, run

python main.py --method mcnn
python main.py --method stan
python main.py --method stagn

Configuration files can be found in config/mcnn_cfg.yaml, config/stan_cfg.yaml and config/stagn_cfg.yaml, respectively.

Models in GTAN and RGTAN can be run via:

python main.py --method gtan
python main.py --method rgtan

For specification of hyperparameters, please refer to config/gtan_cfg.yaml and config/rgtan_cfg.yaml.

Model in HOGRL can be run via:

python main.py --method hogrl

For specification of hyperparameters, please refer to config/hogrl_cfg.yaml.

Data Description

There are three datasets, YelpChi, Amazon and S-FFSD, utilized for model experiments in this repository.

YelpChi and Amazon datasets are from CARE-GNN, whose original source data can be found in this repository.

S-FFSD is a simulated & small version of finacial fraud semi-supervised dataset. Description of S-FFSD are listed as follows:

We are looking for interesting public datasets! If you have any suggestions, please let us know!

Test Result

The performance of five models tested on three datasets are listed as follows:

MCNN, STAN and STAGN are presently not applicable to YelpChi and Amazon datasets.

HOGRL is presently not applicable to S-FFSD dataset.

Repo Structure

The repository is organized as follows:

• models/: the pre-trained models for each method. The readers could either train the models by themselves or directly use our pre-trained models;

• data/: dataset files;

• config/: configuration files for different models;

• feature_engineering/: data processing;

• methods/: implementations of models;

• main.py: organize all models;

• requirements.txt: package dependencies;

Requirements

python           3.7
scikit-learn     1.0.2
pandas           1.3.5
numpy            1.21.6
networkx         2.6.3
scipy            1.7.3
torch            1.12.1+cu113
dgl-cu113        0.8.1

Contributors :

Citing

If you find Antifraud is useful for your research, please consider citing the following papers:

@inproceedings{zou2024effective,
  title={Effective High-order Graph Representation Learning for Credit Card Fraud Detection.},
  author={Zou, Yao and Cheng, Dawei},
  booktitle={International Joint Conference on Artificial Intelligence},
  year={2024}
}
@inproceedings{Xiang2023SemiSupervisedCC,
    title={Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation},
    author={Sheng Xiang and Mingzhi Zhu and Dawei Cheng and Enxia Li and Ruihui Zhao and Yi Ouyang and Ling Chen and Yefeng Zheng},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    year={2023}
}
@article{cheng2020graph,
    title={Graph Neural Network for Fraud Detection via Spatial-temporal Attention},
    author={Cheng, Dawei and Wang, Xiaoyang and Zhang, Ying and Zhang, Liqing},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    year={2020},
    publisher={IEEE}
}
@inproceedings{cheng2020spatio,
    title={Spatio-temporal attention-based neural network for credit card fraud detection},
    author={Cheng, Dawei and Xiang, Sheng and Shang, Chencheng and Zhang, Yiyi and Yang, Fangzhou and Zhang, Liqing},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={34},
    number={01},
    pages={362--369},
    year={2020}
}
@inproceedings{fu2016credit,
    title={Credit card fraud detection using convolutional neural networks},
    author={Fu, Kang and Cheng, Dawei and Tu, Yi and Zhang, Liqing},
    booktitle={International Conference on Neural Information Processing},
    pages={483--490},
    year={2016},
    organization={Springer}
}