Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
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Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
UNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVEL Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model LOPMENT ECONOMICSCONSTRUCT CREDIT SCORING MODELS USINGLOGISTIC REGRESSION, NEURAL NETWORK ANDTHEHYBRID MODELBYLE MIMI TIENMASTER OF ARTS INDEVELOPMENT ECONOMICSHO CHI MINH CITY, NOV EMBER 20151UNIVERSITY OF ECONOMICSHO CHI MINH CITYVIETNAMINSTITl TE OF SOCIAL STUDIESTHE HAGUETHE NETHERLANDSVIETNAM - Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model NETHERLANDSPROGRAMME FOE M.A. IN DEVELOPMENT ECONOMICSCONSTRUCT CREDIT SCORING MODELS USING LOGISTIC REGRESSION, NEURAL NETWORK ANDTHE HYBRID MODELALuận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS INDEVELOPMENT ECONOMICSByLE MINH TIENAcademic Supervisor:DUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVEL Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model eading to the non pel forming loan ratio of Banks increases. In the period 2007 to 2014. Viet Nam have seen a downtrend in credit growth from 53.89% in 2007 to 11.8% in 2014 without signs of strong recovery in the next period. A decline of credit growth implies that enterprises are facing difficult Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model 111 approaching credit from lending institutions and those enterprises winch operate mainly base on credit will be strongest affected ones. Non perforLuận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
ming loan ratio of Banks 111 Viet Nam has increased 111 2007 to 2014. from 2% in 2007 then reached 3.25% in 2014 (highest in 2012 at 4.08%). In this pUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVEL Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model strongly with domestic and foreign ones to achieve shares and maintain profit at the current. Viet Nam is known as a densely populated country (a market size of 90 million people and high proportion of young people) which is considered as a potential retail market for Banks to expand and develop in Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model the next period. To increase the competitiveness of Banks and also improve effective loan risk management, this study applied different methods that aLuận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
re common used to build up credit scoring model such as logistic regression, neural network and hybrid model. Credit scoring model is considered as anUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVEL Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model lysis process of Banks. Final results confirmed that characteristics like age. education, marital status, cunent living status, living time in the current place, type of job. working time ill current job, working time in current field, number of dependent people, historical payment have a statistica Luận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model lly significant effect on repayment capacity of a customer Credit scoring models can classify customers according to different strategic purposes of uLuận văn thạc sĩ construct credit scoring models using logistic regression, neural network and the hybrid model
sers. And the performance of hybrid models seemed better and more reliable than separate ones.3UNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVELUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STL DIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE VIA. IN DEVELGọi ngay
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