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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Abdou HA, Tsafack MDD, Ntim CG, et al., 2016. Predicting creditworthiness in retail banking with limited scoring data. Knowl-Based Syst, 103:89–103. https://doi.org/10.1016/j.knosys.2016.03.023
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. https://doi.org/10.1109/CIFER.1997.618940
Andreas J, Rohrbach M, Darrell T, et al., 2016. Learning to compose neural networks for question answering. https://arxiv.org/abs/1601.01705
Angelini E, di Tollo G, Roli A, 2008. A neural network approach for credit risk evaluation. Q Rev Econom Finan, 48(4):733–755. https://doi.org/10.1016/j.qref.2007.04.001
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. https://doi.org/10.1007/s00521-010-0362-z
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.
Bolton RJ, Hand DJ, 2001. Unsupervised profiling methods for fraud detection. Proc Credit Scoring and Credit Control VII, p.5–7.
Bordes A, Chopra S, Weston J, 2014. Question answering with subgraph embeddings. https://arxiv.org/abs/1406.3676
Bordes A, Usunier N, Chopra S, et al., 2015. Large-scale simple question answering with memory networks. https://arxiv.org/abs/1506.02075
Buterin V, 2014. A Next Generation Smart Contract & Decentralized Application Platform. Ethereum White Paper. 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
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
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. https://doi.org/10.1109/RAIT.2016.7507897
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.
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. {rs https://doi.org/10.3115/v1/P15-1026
Etzioni O, 2011. Search needs a shake-up. Nature, 476(7358): 25–26. https://doi.org/10.1038/476025a
Graves A, Wayne G, Reynolds M, et al., 2016. Hybrid computing using a neural network with dynamic external memory. Nature, 538(7626):471–476. https://doi.org/10.1038/nature20101
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. https://doi.org/10.1145/2939672.2939754
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
Ha VS, Nguyen HN, 2016. Credit scoring with a feature selection approach based deep learning. MATEC Web of Conf, Article 54. https://doi.org/10.1051/matecconf/20165405004
Hamrick JB, Ballard AJ, Pascanu R, et al., 2017. Metacontrol for adaptive imagination-based optimization. {rs https://arxiv.org/abs/1705.02670} url
Han L, Han LY, Zhao HW, 2013. Orthogonal support vector machine for credit scoring. Eng Appl Artif Intell, 26(2): 848–862. https://doi.org/10.1016/j.engappai.2012.10.005
He X, Liao L, Zhang H, et al., 2017. Neural collaborative filtering. Proc 26th Int Conf on World Wide Web, p.173–182.
Heaton JB, Polson NG, Witte JH, 2016a. Deep learning in finance. https://arxiv.org/abs/1602.06561
Heaton JB, Polson NG, Witte JH, 2016b. Deep portfolio theory. https://arxiv.org/abs/1605.07230
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.
Jiang ZY, Xu DX, Liang JJ, 2017. A deep reinforcement learning framework for the financial portfolio management problem. https://arxiv.org/abs/1706.10059
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.
Khandani AE, Kim AJ, Lo AW, 2010. Consumer credit-risk models via machine-learning algorithms. J Bank Finan, 34(11):2767–2787. https://doi.org/10.1016/j.jbankfin.2010.06.001
Khashman A, 2010. Neural networks for credit risk evaluation: investigation of different neural models and learning schemes. Expert Syst Appl, 37(9):6233–6239. https://doi.org/10.1016/j.eswa.2010.02.101
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. https://doi.org/10.1109/ICCVW.2015.52
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. https://doi.org/10.1016/j.ejor.2006.08.043
Li JW, Monroe W, Shi TL, et al., 2017. Adversarial learning for neural dialogue generation. https://arxiv.org/abs/1701.06547
Li XJ, Lipton ZC, Dhingra B, et al., 2016. A user simulator for task-completion dialogues. https://arxiv.org/abs/1612.05688
Li XJ, Chen YN, Li LH, et al., 2017. End-to-end taskcompletion neural dialogue systems. https://arxiv.org/abs/1703.01008
Micali S, 2016. ALGORAND: the efficient and democratic ledger. https://arxiv.org/abs/1607.01341
Min SH, Lee J, Han I, 2006. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl, 31(3):652–660. https://doi.org/10.1016/j.eswa.2005.09.070
Nakamoto S, 2009. Bitcoin: a Peer-to-Peer Electronic Cash System. Bitcoin White Paper.
Olson DL, Delen D, Meng YY, 2012. Comparative analysis of data mining methods for bankruptcy prediction. Dec Support Syst, 52(2):464–473. https://doi.org/10.1016/j.dss.2011.10.007
Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409–413. https://doi.org/10.1016/J.ENG.2016.04.018
Parkes DC, Wellman MP, 2015. Economic reasoning and artificial intelligence. Science, 349(6245):267–272. https://doi.org/10.1126/science.aaa8403
Reed SL, 2014. Bitcoin cooperative proof-of-stake. https://arxiv.org/abs/1405.5741
Ribeiro LFR, Savarese PHP, Figueiredo DR, 2017. struc2vec: learning node representations from structural identity. https://arxiv.org/abs/1704.03165
Rosenfeld M, 2014. Analysis of hashrate-based double spending. https://arxiv.org/abs/1402.2009
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. https://doi.org/10.1109/SIEDS.2017.7937700
Salinas D, Gasthaus J, Flunkert V, 2017. DeepAR: probabilistic forecasting with autoregressive recurrent networks. https://arxiv.org/abs/1704.04110
Shen WW, Wang J, 2016. Portfolio blending via Thompson sampling. Proc 25th Int Joint Conf on Artificial Intelligence, p.1983–1989.
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.
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. https://doi.org/10.1631/FITEE.1700826
Stoica I, Song D, Popa RA, et al., 2017. A Berkeley view of systems challenges for AI. https://arxiv.org/abs/1712.05855
Sun Y, Wang XG, Tang XO, 2014a. Deep learning face representation by joint identification-verification. https://arxiv.org/abs/1406.4773
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. https://doi.org/10.1109/CVPR.2014.244
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. https://doi.org/10.1109/CVPR.2014.220
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. https://doi.org/10.1504/IJMC.2017.10003253
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. https://doi.org/10.1016/j.knosys.2012.04.004
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. https://doi.org/10.3115/v1/P15-1128
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. https://doi.org/10.1145/3285029
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
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. https://doi.org/10.1109/ICDM.2014.104
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. https://doi.org/10.1145/2939672.2939861
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. https://doi.org/10.1145/3097983.3098029
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. https://doi.org/10.1631/FITEE.1800580
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Project supported by the National Natural Science Foundation of China (No. U1509221), the National Key Technology R&D Program of China (No. 2015BAH07F01), and the Zhejiang Provincial Key R&D Program, China (No. 2017C03044)
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Zheng, Xl., Zhu, My., Li, Qb. et al. FinBrain: when finance meets AI 2.0. Frontiers Inf Technol Electronic Eng 20, 914–924 (2019). https://doi.org/10.1631/FITEE.1700822
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DOI: https://doi.org/10.1631/FITEE.1700822