Full Text:   <384>

Summary:  <89>

CLC number: TP391

On-line Access: 2019-08-05

Received: 2017-12-12

Revision Accepted: 2018-05-25

Crosschecked: 2019-07-22

Cited: 0

Clicked: 1464

Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-lin Zheng


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.7 P.914-924


FinBrain: when finance meets AI 2.0

Author(s):  Xiao-lin Zheng, Meng-ying Zhu, Qi-bing Li, Chao-chao Chen, Yan-chao Tan

Affiliation(s):  Department of Computer Science, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   xlzheng@zju.edu.cn

Key Words:  Artificial intelligence, Financial intelligence

Xiao-lin Zheng, Meng-ying Zhu, Qi-bing Li, Chao-chao Chen, Yan-chao Tan. FinBrain: when finance meets AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 914-924.

@article{title="FinBrain: when finance meets AI 2.0",
author="Xiao-lin Zheng, Meng-ying Zhu, Qi-bing Li, Chao-chao Chen, Yan-chao Tan",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T FinBrain: when finance meets AI 2.0
%A Xiao-lin Zheng
%A Meng-ying Zhu
%A Qi-bing Li
%A Chao-chao Chen
%A Yan-chao Tan
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 7
%P 914-924
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700822

T1 - FinBrain: when finance meets AI 2.0
A1 - Xiao-lin Zheng
A1 - Meng-ying Zhu
A1 - Qi-bing Li
A1 - Chao-chao Chen
A1 - Yan-chao Tan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 7
SP - 914
EP - 924
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700822

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.

金融大脑:当金融遇见AI 2.0

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。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abdou HA, Tsafack MDD, Ntim CG, et al., 2016. Predicting creditworthiness in retail banking with limited scoring data. Knowl-Based Syst, 103:89-103.

[2]Aleskerov E, Freisleben B, Rao B, 1997. CARDWATCH: a neural network based database mining system for credit card fraud detection. Proc IEEE/IAFE Computational Intelligence for Financial Engineering, p.220-226.

[3]Andreas J, Rohrbach M, Darrell T, et al., 2016. Learning to compose neural networks for question answering. https://arxiv.org/abs/1601.01705

[4]Angelini E, di Tollo G, Roli A, 2008. A neural network approach for credit risk evaluation. Q Rev Econom Finan, 48(4):733-755.

[5]Bahrammirzaee A, 2010. A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neur Comput Appl, 19(8):1165-1195.

[6]Berant J, Chou A, Frostig R, et al., 2013. Semantic parsing on freebase from question-answer pairs. Proc Conf on Empirical Methods in Natural Language Processing, p.1533-1544.

[7]Bolton RJ, Hand DJ, 2001. Unsupervised profiling methods for fraud detection. Proc Credit Scoring and Credit Control VII, p.5-7.

[8]Bordes A, Chopra S, Weston J, 2014. Question answering with subgraph embeddings. https://arxiv.org/abs/1406.3676

[9]Bordes A, Usunier N, Chopra S, et al., 2015. Large-scale simple question answering with memory networks. https://arxiv.org/abs/1506.02075

[10]Buterin V, 2014. A Next Generation Smart Contract & Decentralized Application Platform. Ethereum White Paper.

[11]Chow Y, Tamar A, Mannor S, et al., 2015. Risk-sensitive and robust decision-making: a CVaR optimization approach. https://arxiv.org/abs/1506.02188

[12]Dhingra B, Li LH, Li XJ, et al., 2016. End-to-end reinforcement learning of dialogue agents for information access. https://arxiv.org/abs/1609.00777

[13]Dineshreddy V, Gangadharan GR, 2016. Towards an “Internet of Things” framework for financial services sector. Proc 3rd Int Conf on Recent Advances in Information Technology, p.177-181.

[14]Ding X, Zhang Y, Liu T, et al., 2015. Deep learning for event-driven stock prediction. Proc 24th Int Conf on Artificial Intelligence, p.2327-2333.

[15]Dong L, Wei FR, Zhou M, et al., 2015. Question answering over freebase with multi-column convolutional neural networks. Proc 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int Joint Conf on Natural Language Processing, p.260-269.

[16]Etzioni O, 2011. Search needs a shake-up. Nature, 476(7358): 25-26.

[17]Graves A, Wayne G, Reynolds M, et al., 2016. Hybrid computing using a neural network with dynamic external memory. Nature, 538(7626):471-476.

[18]Grover A, Leskovec J, 2016. node2vec: scalable feature learning for networks. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.855-864.

[19]Guo HF, Tang RM, Ye YM, et al., 2017. DeepFM: a factorization-machine based neural network for CTR prediction. https://arxiv.org/abs/1703.04247

[20]Ha VS, Nguyen HN, 2016. Credit scoring with a feature selection approach based deep learning. MATEC Web of Conf, Article 54.

[21]Hamrick JB, Ballard AJ, Pascanu R, et al., 2017. Metacontrol for adaptive imagination-based optimization. https://arxiv.org/abs/1705.02670

[22]Han L, Han LY, Zhao HW, 2013. Orthogonal support vector machine for credit scoring. Eng Appl Artif Intell, 26(2): 848-862.

[23]He X, Liao L, Zhang H, et al., 2017. Neural collaborative filtering. Proc 26th Int Conf on World Wide Web, p.173-182.

[24]Heaton JB, Polson NG, Witte JH, 2016a. Deep learning in finance. https://arxiv.org/abs/1602.06561

[25]Heaton JB, Polson NG, Witte JH, 2016b. Deep portfolio theory. https://arxiv.org/abs/1605.07230

[26]Hoofnagle CJ, 2014. How the fair credit reporting act regulates big data. Future of Privacy Forum Workshop on Big Data and Privacy: Making Ends Meet.

[27]Jiang ZY, Xu DX, Liang JJ, 2017. A deep reinforcement learning framework for the financial portfolio management problem. https://arxiv.org/abs/1706.10059

[28]Kedia S, Monga EG, 2017. Static signature matching using LDA and artificial neural networks. Int J Adv Res Ideas Innov Technol, 3(3):245-248.

[29]Khandani AE, Kim AJ, Lo AW, 2010. Consumer credit-risk models via machine-learning algorithms. J Bank Finan, 34(11):2767-2787.

[30]Khashman A, 2010. Neural networks for credit risk evaluation: investigation of different neural models and learning schemes. Expert Syst Appl, 37(9):6233-6239.

[31]Kuang ZH, Huang C, Zhang W, 2015. Deeply learned rich coding for cross-dataset facial age estimation. IEEE Int Conf on Computer Vision Workshop, p.338-343.

[32]Kumar PR, Ravi V, 2007. Bankruptcy prediction in banks and firms via statistical and intelligent techniques—a review. Eur J Oper Res, 180(1):1-28.

[33]Li JW, Monroe W, Shi TL, et al., 2017. Adversarial learning for neural dialogue generation. https://arxiv.org/abs/1701.06547

[34]Li XJ, Lipton ZC, Dhingra B, et al., 2016. A user simulator for task-completion dialogues. https://arxiv.org/abs/1612.05688

[35]Li XJ, Chen YN, Li LH, et al., 2017. End-to-end task-completion neural dialogue systems. https://arxiv.org/abs/1703.01008

[36]Micali S, 2016. ALGORAND: the efficient and democratic ledger. https://arxiv.org/abs/1607.01341

[37]Min SH, Lee J, Han I, 2006. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl, 31(3):652-660.

[38]Nakamoto S, 2009. Bitcoin: a Peer-to-Peer Electronic Cash System. Bitcoin White Paper.

[39]Olson DL, Delen D, Meng YY, 2012. Comparative analysis of data mining methods for bankruptcy prediction. Dec Support Syst, 52(2):464-473.

[40]Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[41]Parkes DC, Wellman MP, 2015. Economic reasoning and artificial intelligence. Science, 349(6245):267-272.

[42]Reed SL, 2014. Bitcoin cooperative proof-of-stake. https://arxiv.org/abs/1405.5741

[43]Ribeiro LFR, Savarese PHP, Figueiredo DR, 2017. struc2vec: learning node representations from structural identity. https://arxiv.org/abs/1704.03165

[44]Rosenfeld M, 2014. Analysis of hashrate-based double spending. https://arxiv.org/abs/1402.2009

[45]Rushin G, Stancil C, Sun MY, et al., 2017. Horse race analysis in credit card fraud—deep learning, logistic regression, and gradient boosted tree. Systems and Information Engineering Design Symp, p.117-121.

[46]Salinas D, Gasthaus J, Flunkert V, 2017. DeepAR: probabilistic forecasting with autoregressive recurrent networks. https://arxiv.org/abs/1704.04110

[47]Shen WW, Wang J, 2016. Portfolio blending via Thompson sampling. Proc 25th Int Joint Conf on Artificial Intelligence, p.1983-1989.

[48]Shen WW, Wang J, Jiang YG, et al., 2015. Portfolio choices with orthogonal bandit learning. Proc 24th Int Conf on Artificial Intelligence, p.974-980.

[49]Shum HY, He XD, Li D, 2018. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Front Inform Technol Electron Eng, 19(1):10-26.

[50]Stoica I, Song D, Popa RA, et al., 2017. A Berkeley view of systems challenges for AI. https://arxiv.org/abs/1712.05855

[51]Sun Y, Wang XG, Tang XO, 2014a. Deep learning face representation by joint identification-verification. https://arxiv.org/abs/1406.4773

[52]Sun Y, Wang XG, Tang XO, 2014b. Deep learning face representation from predicting 10,000 classes. IEEE Conf on Computer Vision and Pattern Recognition, p.1891-1898.

[53]Taigman Y, Yang M, Ranzato M, et al., 2014. DeepFace: closing the gap to human-level performance in face verification. IEEE Conf on Computer Vision and Pattern Recognition, p.1701-1708.

[54]Wang F, Zhou JY, Chen D, et al., 2017. Research on mobile commerce payment management based on the face biometric authentication. Int J Mob Commun, 15(3):278-305.

[55]Yeh CC, Lin FY, Hsu CY, 2012. A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowl-Based Syst, 33:166-172.

[56]Yih WT, Chang MW, He XD, et al., 2015. Semantic parsing via staged query graph generation: question answering with knowledge base. Proc 53rd Meeting of the Association for Computational Linguistics and the 7th Int Joint Conf on Natural Language Processing, p.1321-1331.

[57]Zhang S, Yao LN, Sun AX, et al., 2017. Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv, 52(1), Article 5.

[58]Zhang YZ, Liu K, He SZ, et al., 2016. Question answering over knowledge base with neural attention combining global knowledge information. https://arxiv.org/abs/1606.00979

[59]Zhao HK, Wu L, Liu Q, et al., 2014. Investment recommendation in P2P lending: a portfolio perspective with risk management. IEEE Int Conf on Data Mining, p.1109-1114.

[60]Zhao HK, Liu Q, Wang GF, et al., 2016. Portfolio selections in P2P lending: a multi-objective perspective. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.2075-2084.

[61]Zhou J, Li XL, Zhao PL, et al., 2017. KunPeng: parameter server based distributed learning systems and its applications in Alibaba and Ant Financial. Proc 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1693-1702.

[62]Zhou H, Chai HF, Qiu ML, 2018. Fraud detection within bankcard enrollment on mobile device based payment using machine learning. Front Inform Technol Electron Eng, 19(12):1537-1545.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE