A hybrid framework of anomaly detection for mutual fund parent companies

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.