(LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model
(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 STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELO (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model OPMENT ECONOMICSCONSTRUCT CREDIT SCORING MODELS USINGLOGISTIC REGRESSION, NEURAL NETWORK ANDTHEHYBRID MODELBYLE MINH TIENMASTER OF ARTS LN DEVELOPMENT ECONOMICSHO CHI MINH CITY, NOVEMBER 20151UNIVERSITY OF ECONOMICSHO ( HI MINH CITYVIETNAMINSTITUTE 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 AND THE HYBRID MODELA(LUẬN 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 STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELO (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model eading to the non performing loan ratio of Banks increases. In (he period 2007 to 2014. Viet Nam have seen a downkend 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 in (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model approaching credit from lending institutions and those enterprises which operate mainly base on credit will be strongest affected ones. Non performin(LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model
g loan ratio of Banks in Viet Nam has increased in 2007 to 2014. from 2% in 2007 then reached 3.25% in 2014 (highest in 2012 at 4.08%). Tn this periodUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELO (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model ngly 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 the (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model next period. To increase the competitiveness of Banks and also improve effective loan risk management, this study applied different methods that are c(LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model
ommon used to build up credit scoring model such as logistic regression, neural network and hybrid model. Credit scoring model is considered as an appUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELO (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model process of Banks. Final results confirmed that characteristics like age. education, marital status, current living status, living time in the current place, type of job. working time in current job. working time in current field, number of dependent people, historical payment have a statistically- s (LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model ignificant effect on repayment capacity’ of a customer. Credit scoring models can classify- customers according to different strategic purposes of use(LUẬN văn THẠC sĩ) construct credit scoring models using logistic regression, neural network and the hybrid model
rs. And the performance of hybrid models seemed better and more reliable than separate ones.3ContentUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELOUNIVERSITY OF EC ONOMICSHO ( HI MINH CITY VIETNAMINSTITUTE OF SOCIAL STUDIESTHE HAGUE THE NETHERLANDSVIETNAM - NETHERLANDSPROGRAMME FOE M.A. IN DEVELOGọi ngay
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