FinBrain: when finance meets AI 2.0

Xiao-lin ZHENG; Meng-ying ZHU; Qi-bing LI; Chao-chao CHEN; Yan-chao TAN

doi:10.1631/FITEE.1700822

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FinBrain: when finance meets AI 2.0

Regular Papers | Updated：2022-05-19

- FinBrain: when finance meets AI 2.0
- 金融大脑：当金融遇见AI 2.0
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 7, Pages: 914-924(2019)
- Affiliations：
  
  Department of Computer Science, Zhejiang University, Hangzhou 310027, China
  AI Department, Ant Financial Services Group, Hangzhou 310027, China
- Author bio：
  
  Xiao-lin ZHENG, E-mail: xlzheng@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1700822
  CLC： TP391
- Received：12 December 2017，
  
  Revised：2019-;7-22，
  
  Published：2019-07
- Accepted：
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Xiao-lin ZHENG, Meng-ying ZHU, Qi-bing LI, et al. FinBrain: when finance meets AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 914-924.
DOI：

Xiao-lin ZHENG, Meng-ying ZHU, Qi-bing LI, et al. FinBrain: when finance meets AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 914-924. DOI： 10.1631/FITEE.1700822.

摘要

人工智能（AI）是技术革命和产业转型的核心技术。金融智能作为AI 2.0时代新需求之一，引起学术界和工业界广泛关注。在当前充满活力的资本市场中，金融智能展示了快速准确的机器学习能力，可处理复杂数据，并有潜力逐渐成为"金融大脑"。我们对现有金融智能进行总结和综述：首先，论述金融智能概念，阐述其在金融技术领域的地位。其次，介绍金融智能的细分领域，回顾财富管理、风险管理、金融安全、金融智能客服和区块链等领域的最新技术。最后，提出一个称作"金融大脑"（FinBrain）的研究框架，总结了4个开放性问题，即可解释的金融代理和因果关系、不确定性下的感知和预测、风险敏感和稳健决策以及多智能体博弈和机制设计。相信这些研究方向可为AI2.0在金融领域的发展奠定基础。

Abstract

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.

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