Year: 2025
Type: article
Abstract: In this paper, we introduce a unique task, the assessment of the mutual fund parent companies , in our financial company, where the anomaly events associated with parent companies need to be identified and sent to financial experts to access the impact on related mutual funds. We propose a hybrid framework of anomaly detection to combine data-driven detection and experts-engaged tuning to enhance the identification process. Our experiments have demonstrated its effectiveness through the feedback from financial experts, utilizing a tracking record spanning from May 2022 to June 2023.
Source: Discover Data
Authors: David Xuejun Wang, Yong Zheng
Institutions: Software602 (Czechia), Illinois Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,390
OpenAlex ID: W4409181268