Toward intelligent financial advisors for identifying potential clients: A multitask perspective

نویسندگان

چکیده

Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue IFAs, i.e., users who are willing purchase the portfolios. Thus, extracting useful information from various characteristics of further predicting their inclination urgent. However, two critical problems encountered real practice make this prediction task challenging, sample selection bias data sparsity. study, we formalize conversion relationship, user ! activated client decompose relationship into three related tasks. Then, propose Multitask Feature Extraction Model (MFEM), which can leverage contained these tasks learn them jointly, thereby solving simultaneously. addition, design two-stage feature algorithm select highly relevant features efficiently accurately an incredibly huge number fields. Finally, conduct extensive experiments on dataset provided famous fintech bank. Experimental results clearly demonstrate effectiveness MFEM.

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ژورنال

عنوان ژورنال: Big data mining and analytics

سال: 2022

ISSN: ['2096-0654']

DOI: https://doi.org/10.26599/bdma.2021.9020021