Skip to main content

FinBrain: when finance meets AI 2.0

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.

This is a preview of subscription content, access via your institution.

References

  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. https://doi.org/10.1016/j.knosys.2016.03.023

    Article  Google Scholar 

  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. https://doi.org/10.1109/CIFER.1997.618940

    Google Scholar 

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

    Book  Google Scholar 

  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. https://doi.org/10.1016/j.qref.2007.04.001

    Article  Google Scholar 

  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. https://doi.org/10.1007/s00521-010-0362-z

    Article  Google Scholar 

  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.

    Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Google Scholar 

  10. 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

    Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Google Scholar 

  13. 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.

    Google Scholar 

  14. 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

    Google Scholar 

  15. Etzioni O, 2011. Search needs a shake-up. Nature, 476(7358): 25–26. https://doi.org/10.1038/476025a

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. 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

    Google Scholar 

  18. 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

    Google Scholar 

  19. 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

    Book  Google Scholar 

  20. Hamrick JB, Ballard AJ, Pascanu R, et al., 2017. Metacontrol for adaptive imagination-based optimization. {rs https://arxiv.org/abs/1705.02670} url

    Google Scholar 

  21. 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

    Article  Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  25. 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.

    Google Scholar 

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

    Google Scholar 

  27. 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.

    Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. 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

    Google Scholar 

  31. 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

    MATH  Article  Google Scholar 

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

    Book  Google Scholar 

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

    Google Scholar 

  34. Li XJ, Chen YN, Li LH, et al., 2017. End-to-end taskcompletion neural dialogue systems. https://arxiv.org/abs/1703.01008

    Google Scholar 

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

    Google Scholar 

  36. 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

    Article  Google Scholar 

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

    Google Scholar 

  38. 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

    Article  Google Scholar 

  39. Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409–413. https://doi.org/10.1016/J.ENG.2016.04.018

    Article  Google Scholar 

  40. Parkes DC, Wellman MP, 2015. Economic reasoning and artificial intelligence. Science, 349(6245):267–272. https://doi.org/10.1126/science.aaa8403

    MathSciNet  MATH  Article  Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Google Scholar 

  44. 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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  47. 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.

    Google Scholar 

  48. 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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  51. 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

    Google Scholar 

  52. 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

    Google Scholar 

  53. 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

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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

    Google Scholar 

  56. 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

    Google Scholar 

  57. 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

    Google Scholar 

  58. 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

    Google Scholar 

  59. 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

    Google Scholar 

  60. 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

    Google Scholar 

  61. 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

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiao-lin Zheng.

Additional information

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Key words

  • Artificial intelligence
  • Financial intelligence

CLC number

  • TP391