Bank Credit Risk Management Using Machine Learning Algorithms
Keywords:
Type — Machine learning, SVM, Credit Risk Software Development, Neural Networks, Support Vector MachinesAbstract
Prior PCs was simply sorted as a need of an individual yet now it turns into a need of a person. AI fills in as a significant part in field of PC. Machine can't thoroughly consider various circumstances however it can draw diverse kind of connections between various highlights and qualities. The significant piece of our life is to stay away from false exercises yet till now we can't authority over it. Credit business is one of the significant organizations of business banks. Deceitful exercises can be handle through installing AI calculations in our everyday life. In this venture we will utilize directed AI and for that we need to give named information to the AI calculation. This paper centers around anticipating SME client status for time of a half year by using application scoring extra to client conduct highlights. By using Neural Networks, Support Vector Machines and Inclination Boosting, execution examination and furthermore highlight investigation for client conduct are directed.
References
. Jochen Kruppa a,1 , Alexandra Schwarz b,1 , Gerhard Arminger b "Consumer credit risk: Individual probability estimates using machine learning"Expert Systems with Applications ELSEVIER 2013.
. Girija V. Attigeri, M. M. Manohara Pai∗, and Radhika M. Pai " Credit Risk Assessment Using Machine Learning Algorithms"American Scientific Publishers 2017.
. Gang Wang , Jian Ma " A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine" Expert Systems with Applications ELSEVIER 2012.
. Martin Leo * , Suneel Sharma and K. Maddulety "Machine Learning in Banking Risk Management: A Literature Review" risks MDPI 2019
. Carol Anne Hargreaves "Machine learning application to identify good credit customers"International Journal of Advanced Engineering and Technology 2019
. Jianhui Yang, Qiman Li, Dongsheng Luo "Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction" Open Journal of Business and Management, 2019
. Sonali Bakshi “Credit Card Fraud Detection " International conference on I-SMAC IEEE 2018.
. Shiivong Birla1, 2, Kashish Kohli1, 2, Akash Dutta "Machine Learning on Imbalanced Data in Credit Risk "IEEE 2016
. Vinod Kumar L, Natarajan S, Keerthana S "Credit Risk Analysis in Peer-to-Peer Lending System " IEEE 2016
. Chun F. Hsu, “Classification Methods of Credit Rating – A Comparative Analysis on SVM, MDA and RST” in Computational Intelligence and Software Engineering, 2009.
CiSE 2009. International Conference, Wuhan, China.
. M.J. Pazzani, "Knowledge discovery from data? " in IEEE Intelligent Systems and their Applications (Volume: 15, Issue: 2, Mar/Apr 2000).
. Qinrong Meng, “Evaluation of Residential Loan by Combining RVM and Logistic Regression” in Management and Service Science (MASS), 2011 International Conference,
Wuhan, China.
. Aslı Çaliş, Ahmet Boyaci, Kasım Baynal, “Data mining application in banking sector with clustering and classification methods” in Industrial Engineering and Operations Management (IEOM), 2015 International Conference,Dubai, United Arab Emirates.
. Yu Zhang, Guang Yu, Yong-sheng Guan, Dong-hui Yang, “Feature selection of nonperforming loans in Chinese commercial banks” in Management Science & Engineering
(ICMSE), 2014 International Conference, Helsinki, Finland.
. Changling Yin,” Fuzzy pattern recognition model of bank loan risk and its application” in Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference,
Yantai, Shandong
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