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,

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.

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.

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: |Name|Type|Range|Note| |--|--|--|--| |Time|np.int32|from $\mathbf{0}$ to $\mathbf{N}$|$\mathbf{N}$ denotes the number of trasactions. | |Source|string|from $\mathbf{S_0}$ to $\mathbf{S}{ns}$|$ns$ denotes the number of transaction senders.| |Target|string|from $\mathbf{T_0}$ to $\mathbf{T}{nt}$ | $nt$ denotes the number of transaction reveicers.| |Amount|np.float32|from 0.00 to np.inf|The amount of each transaction. | |Location|string|from $\mathbf{L_0}$ to $\mathbf{L}{nl}$ |$nl$ denotes the number of transacation locations.| |Type|string|from $\mathbf{TP_0}$ to $\mathbf{TP}{np}$|$np$ denotes the number of different transaction types. | |Labels|np.int32|from 0 to 2|2 denotes unlabeled||

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: | |YelpChi| | |Amazon| | |S-FFSD| | | |:----|:----|:----|:----|:----|:----|:----|:----|:----|:----| | |AUC|F1|AP|AUC|F1|AP|AUC|F1|AP| |MCNN||- | -| -| -| -|0.7129|0.6861|0.3309| |STAN|- |- | -| -| -| -|0.7422|0.6698|0.3324| |STAGN|- |- | -| -| -| -|0.7659|0.6852|0.3599| |GTAN|0.9241|0.7988|0.7513|0.9630|0.9213|0.8838|0.8286|0.7336|0.6585| |RGTAN|0.9498|0.8492|0.8241|0.9705|0.9198|0.8925|0.8461|0.7513|0.6939|

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

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

Citing

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

@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}
}