@inproceedings{3671f8f7e1a6467fb396839fc307d144,
title = "Unveiling the Black Box: An XAI-based Anti-Money Laundering Model",
abstract = "In the existing anti-money-laundering process, judging abnormal transactions still requires human resources, which is time-consuming and requires companies to pay many human costs. Many experts and scholars have used AI to identify abnormal trading behavior of accounts, but the problem of highly unbalanced data leads to poor model performances. In addition, the complex neural network of deep learning models is considered a black box, which is less likely to explain the model's results. Therefore, our research proposed an {"}XAI-based AI Anti-Money Laundering Model.{"}We utilize the DNN model to detect laundering, with a recall of 0.94. By applying SHAP to the model, we evaluate the effectiveness of the dataset's ten features on the model. We find that {"}Payment Format{"}is the most crucial feature of the anti-money laundering model.",
keywords = "Anti-money laundering, Explainable AI, LIME, PDPs, SHAP",
author = "Li, {Pei Yi} and Chang, {Ting Ting} and Kuo, {Yu Chiao} and Lin, {Chia Yu} and Chang, {Heng Yu}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024 ; Conference date: 09-07-2024 Through 11-07-2024",
year = "2024",
doi = "10.1109/ICCE-Taiwan62264.2024.10674363",
language = "???core.languages.en_GB???",
series = "11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "293--294",
booktitle = "11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024",
}