Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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

FinBrain: when finance meets AI 2.0

Artificial intelligence(AI)is the core technology of technological revolution and industrial transformation.As one of the new intelligent needs in the AI 2.0 era,financial intelligence has elicited much attention from the academia and industry.In our current dynamic capital market,financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a'financial brain.'In this paper,we survey existing studies on financial intelligence.First,we describe the concept of financial intelligence and elaborate on its position in the financial technology field.Second,we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management,risk management,financial security,financial consulting,and blockchain.Finally,we propose a research framework called FinBrain and summarize four open issues,namely,explainable financial agents and causality,perception and prediction under uncertainty,risk-sensitive and robust decision-making,and multi-agent game and mechanism design.We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.