AEFA: An Ensemble Framework for Fraud Detection in the Forex Market

RAIDS Lab Authors

Zhengyi Yang

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Abstract

The Foreign Exchange (Forex) market is a decentralized, high-frequency trading environment that is particularly vulnerable to manipulation and fraud. Fraudulent strategies often exploit low-liquidity periods to distort price movements and mislead traders. While traditional fraud detection strategies are simple and interpretable, their effectiveness and efficiency are constrained in real industrial scenarios due to the need for extensive expert verification and significant time investment, making it challenging to identify complex fraud patterns. To tackle these challenges, we propose a Forex market fraud detection (FMFD) framework named Accelerated Ensemble Fraud Analysis (AEFA). It consists of three key components: an Initial Data Optimization module for outlier removal, a Probabilistic Decision Module employing Random Forest for high-recall classification, and an Advanced Voting Integration module with a soft voting strategy. Extensive experiments demonstrate that AEFA outperforms existing methods with superior performance and efficiency, enabling scalable fraud detection in Forex trading systems.

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@inproceedings{wang2025aefa,
  title = {AEFA: An Ensemble Framework for Fraud Detection in the Forex Market},
  author = {Wang, Weiyuan and Yu, Jianke and Yang, Zhengyi and Ju, Mingchen and Yu, Shuyue and Wu, Jinglin and Liu, Lifan and Liu, Yongfei and Shepherd, John and Zhang, Wenjie},
  editor = {Yoshikawa, Masatoshi and Meng, Xiaofeng and Cao, Yang and Xiao, Chuan and Chen, Weitong and Wang, Yanda},
  booktitle = {Advanced Data Mining and Applications},
  year = {2026},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  pages = {34--49},
  isbn = {978-981-95-3459-3}
}