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3 août 2021
- handle: 10670/1.d34e7a
- hal: hal-03322462
Mots-clés
Random forest Logistic regression VARHS Credit assessment Machine learning G - Financial Economics/G.G1 - General Financial Markets/G.G1.G10 - General G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G21 - Banks • Depository Institutions • Micro Finance Institutions • Mortgages Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q1 - Agriculture/Q.Q1.Q14 - Agricultural Finance C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C55 - Large Data Sets: Modeling and AnalysisSujets proches
Funds Funding Borrowing Finance, commerce, confiscations, etcCiter ce document
Hong Nhung Do et al., « A comparison of random forest and logistic regression model in credit scoring of rural households », HAL-SHS : économie et finance, ID : 10670/1.d34e7a
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Résumé
Many banks currently use the logistic regression model to do credit scoring to give loans to customers. This paper compares the random forest and logistic regression methods to support the financial analysis functions of the predictive tool for credit scoring. We use the data provided by the Vietnam Access Resource to Household Survey (VARHS), which contains 3,530 households in the year 2014 in 12 provinces of Vietnam. Results show that random forest proved to be a better accurate predictive tool than the logistic regression method. This suggests banks use the random forest to predict potential lenders based on the existing client dataset resulting in saving time and cost to find potential clients.