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Financial Innovation Based on Artificial Intelligence Technologies

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Published:12 July 2019Publication History

ABSTRACT

Nowadays, the degree of the heated topic of artificial intelligence in the world reaches a new height. Due to the breakthrough of deep learning algorithm based on neural network, the level of artificial intelligence technologies has been enhanced significantly. The global financial industry is quietly changing under the catalysis of artificial intelligence. The frontier artificial intelligence technologies, such as the technology of expert system, machine learning and knowledge discovery in database are combed to explore the financial applications of artificial intelligence. Based on these key technologies, this paper proposed three applications of artificial intelligence in the financial field, including intelligent investment adviser, transaction forecast and financial regulation, discusses the key technologies of artificial intelligence and financial innovation products based on these technologies, such as the functions of the transaction prediction system based on artificial intelligence technologies include forecast analysis, index statistics, stock analysis and information retrieval, etc. The structures of the systems are drawn and the design principles are provided. Finally, to guard the safety of the applications of artificial intelligence, the paper gives the suggestions of enhancing identity authentication, introducing monitoring measures and limiting autonomy degree.

References

  1. H. Rezaei, R. Nazir, E. Momeni (2016). Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study. Journal of Zhejiang University SCIENCE A, 17(4): 273--285.Google ScholarGoogle ScholarCross RefCross Ref
  2. M.A. Shahin (2016). State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers, 7(1): 33--44.Google ScholarGoogle ScholarCross RefCross Ref
  3. F. Yao, L. Ji, C.Y. Zhang, et al. (2011). Real-time virtual reference service based on applicable artificial intelligence technologies: the debut of the robot Xiaotu at Tsinghua university library. Chinese Journal of Library and Information Science, 2: 12--26.Google ScholarGoogle Scholar
  4. V. Nourani, G. Andalib (2015). Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. Journal of Mountain Science, 12(1): 85--100.Google ScholarGoogle ScholarCross RefCross Ref
  5. G. Cao, C. Li, G. Zhou, et al. (2010). Rolling force prediction for strip casting using theoretical model and artificial intelligence. Journal of Central South University of Technology, 17(4): 795--800.Google ScholarGoogle ScholarCross RefCross Ref
  6. R. Viswanathan, P. Samui (2016). Determination of rock depth using artificial intelligence techniques. Geoscience Frontiers, 7(1): 61--66.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Mollaiy-Berneti (2015). Developing energy forecasting model using hybrid artificial intelligence method. Journal of Central South University, 22(8): 3026--3032.Google ScholarGoogle ScholarCross RefCross Ref
  8. J.M. Corchado, W.G. Li, J. Bajo, et al. (2016). Editorial: Special issue on distributed computing and artificial intelligence. Frontiers, 4: 001.Google ScholarGoogle Scholar
  9. Jianping Q, Axing Z, Yongbo C, et al. (2003). A 3D visible evaluation of landslide risk degree under integration of GIS and artificial intelligence. Science in China, Ser E, 46(Supp): 142--147.Google ScholarGoogle Scholar
  10. R. Viswanathan, P. Samui (2016). Determination of rock depth using artificial intelligence techniques. Geoscience Frontiers, 7(1): 61--66.Google ScholarGoogle ScholarCross RefCross Ref
  11. V. Nourani, G. Andalib (2015). Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. Journal of Mountain Science, 12(1): 85--100.Google ScholarGoogle ScholarCross RefCross Ref
  12. S. Mollaiy-Berneti (2015). Developing energy forecasting model using hybrid artificial intelligence method. Journal of Central South University, 22(8): 3026--3032.Google ScholarGoogle ScholarCross RefCross Ref
  13. F. Ren, Y. Gu (2014). ZTE Communications Call for Papers Special Issue on Using Artificial Intelligence in Internet of Things. ZTE Communications, 3: 002.Google ScholarGoogle Scholar
  14. D. Calvanese, G. De Giacomo, D. Lembo, et al. (2013). Data complexity of query answering in description logics. Artificial Intelligence, 195: 335--360. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Zang, F. Zhang, C. Di, et al. (2015). Advances of flexible pressure sensors toward artificial intelligence and health care applications. Materials Horizons, 2(2): 140--156.Google ScholarGoogle Scholar
  16. Y. Gil, M. Greaves, J. Hendler, et al. (2014) Amplify scientific discovery with artificial intelligence. Science, 346(6206): 171--172.Google ScholarGoogle ScholarCross RefCross Ref
  17. M.R. López, C.P. Sánchez, P. de Llano Monelos (2015). Financial risk determination of failure by using parametric model, artificial intelligence and audit information. Estudios de Economía, 41(2): 187--217.Google ScholarGoogle Scholar

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        AICS 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science
        July 2019
        858 pages
        ISBN:9781450371506
        DOI:10.1145/3349341

        Copyright © 2019 ACM

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        Publication History

        • Published: 12 July 2019

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