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: 6361
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,in press.https://doi.org/10.1631/FITEE.1700822 @article{title="FinBrain: when finance meets AI 2.0", %0 Journal Article TY - JOUR
金融大脑:当金融遇见AI 2.0关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[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. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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