Abstract
Artificial intelligence is considered to be the technological commanding height of the next era. At present, after the development of China’s artificial intelligence industry ranks in the United States, its application in the financial field is also in a new stage of rapid development, and affects many aspects of the financial industry, thus strengthening its research is of great significance. The continuous development of artificial intelligence technology has been widely used in many aspects of financial services, which is of great significance for the realization of its modeling, standardization and intelligent development. However, there are still security risks hidden in the application, which requires attention to this. aspects of the research to identify effective measures for risk prevention, this paper analyzes the application of artificial intelligence in the financial sector in the personal credit score.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiang M, Pei XU, Xiao R et al (2015) Research on algorithms development and optimization for personal credit scoring. J Harbin Inst Technol 47(5):40–45
Fuentescabrera J, Pérezvicente H (2015) Credit scoring model for payroll issuers: a real case. In: Mexican international conference on artificial intelligence
Luo C, Wu D, Wu D (2016) A deep learning approach for credit scoring using credit default swaps. Eng Appl Artif Intell 65
Pedro JS, Proserpio D, Oliver N (2015) MobiScore: towards universal credit scoring from mobile phone data, vol 9146, pp 195–207
Ishii K (2017) Comparative legal study on privacy and personal data protection for robots equipped with artificial intelligence: looking at functional and technological aspects. AI Soc (1):1–25
FoxBusiness (2015) Artificial Intelligence scores high at arcade; Google program beats gamers at ‘Space Invaders’. Fox Business
Abidoye RB, Chan APC (2017) Valuers’ receptiveness to the application of artificial intelligence in property valuation. Pac Rim Prop Res J 23(2):1–19
Alaraj M, Abbod MF (2016) Classifiers consensus system approach for credit scoring. Knowl-Based Syst 104:89–105
Qi J, Liu X, Tejedor J et al (2017) Unsupervised submodular rank aggregation on score-based permutations
Zou S (2017) Designing and practice of a college English teaching platform based on artificial intelligence. J Comput Theoret Nanosci 14(1):104–108
Bennett CC (2015) Clinical decision-making artificial intelligence object oriented system and method
Wang S, Cong Y, Cao J et al (2016) Scalable gastroscopic video summarization via similar-inhibition dictionary selection. Artif Intell Med 66:1–13
Haim G, Gal Y, An B et al (2017) Human–computer negotiation in a three player market setting. Artif Intell 246:34–52
Dashtban M, Balafar M (2017) Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2):91–107
Uto M, Ueno M (2018) Item response theory without restriction of equal interval scale for rater’s score. In: International conference on artificial intelligence in education. Springer, Cham, pp 363–368
Duan X, Thomsen NB, Tan ZH et al (2017) Weighted score based fast converging CO-training with application to audio-visual person identification. In: IEEE international conference on TOOLS with artificial intelligence. IEEE Computer Society, pp 610–617
Chang M, Chang M, Chang M et al (2017) iWordNet: a new approach to cognitive science and artificial intelligence. Adv Artif Intell 2017(3):1–10
Yuan E, Dan G, Xuegang HU et al (2016) Frequent pattern mining from biological sequences based on score matrix. Pattern Recogn Artif Intell
Oliver N (2016) Data-driven human behavior models: opportunities and challenges. In: Spanish conference on information retrieval. ACM, p 1
Buza K (2016) ParkinsoNET: estimation of UPDRS score using hubness-aware feedforward neural networks. Appl Artif Intell 30(6):541–555
Acknowledgement
The paper is a periodical achievement of the 2018 scientific research program of Beijing Institute of Technology, Zhuhai (XK-2018-19).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C., Huang, H., Lu, S. (2019). Research on Personal Credit Scoring Model Based on Artificial Intelligence. In: Sugumaran, V., Xu, Z., P., S., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2019. Advances in Intelligent Systems and Computing, vol 929. Springer, Cham. https://doi.org/10.1007/978-3-030-15740-1_64
Download citation
DOI: https://doi.org/10.1007/978-3-030-15740-1_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15738-8
Online ISBN: 978-3-030-15740-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)