Hybrid Decision Models of Leasing Business for Thailand Using Neural Network
The research aims to improve the effectiveness of financial lending business decision-making by developing dynamic models involved in the money-lending business. The objectives of this study are to identify preference factors that affect a customer’s decision of choosing a particular financial insti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11730 |
| _version_ | 1797465978494255104 |
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| author | Nachapong Jiamahasap Sakgasem Ramingwong |
| author_facet | Nachapong Jiamahasap Sakgasem Ramingwong |
| author_sort | Nachapong Jiamahasap |
| collection | DOAJ |
| description | The research aims to improve the effectiveness of financial lending business decision-making by developing dynamic models involved in the money-lending business. The objectives of this study are to identify preference factors that affect a customer’s decision of choosing a particular financial institution, to determine the important approval factors that providers need to take into consideration while approving loans and to identify any relationship between and among the factors. The data are taken from a case study of a lending company in northern Thailand. The first model is the preference model, comprising 68 inputs factors, which are used to determine the reasons why a customer chooses service providers, which can be either commercial or non-commercial banks. The model is developed using a neural network (NN) with a history data of 2973 records and comprising four sub-models. The model is improved by varying the NN structure and EPOC. The best model provides an accuracy rate of 100%. The second model is the approval model, comprising 55 input factors for predicting the result of loan requests, which can determine if the loan should be approved with the full amount of the request, approved with a lesser amount or another outcome. The model is developed using a neural network with history data of 787 records. This model is composed of three sub-models; the best model of which gives an accuracy rate of 55%. The third model is the hybrid decision model, linking preference factors and approval factors with external factors. The model is constructed using system dynamics factors, approval factors, financial institutions and system dynamic modeling and the model can simulate the result if the input is changed. |
| first_indexed | 2024-03-09T18:29:17Z |
| format | Article |
| id | doaj.art-60b204f3dba1414e964350ea8455f5da |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:17Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-60b204f3dba1414e964350ea8455f5da2023-11-24T07:39:58ZengMDPI AGApplied Sciences2076-34172022-11-0112221173010.3390/app122211730Hybrid Decision Models of Leasing Business for Thailand Using Neural NetworkNachapong Jiamahasap0Sakgasem Ramingwong1Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandIndustrial Engineering Department, Chiang Mai University, Chiang Mai 50200, ThailandThe research aims to improve the effectiveness of financial lending business decision-making by developing dynamic models involved in the money-lending business. The objectives of this study are to identify preference factors that affect a customer’s decision of choosing a particular financial institution, to determine the important approval factors that providers need to take into consideration while approving loans and to identify any relationship between and among the factors. The data are taken from a case study of a lending company in northern Thailand. The first model is the preference model, comprising 68 inputs factors, which are used to determine the reasons why a customer chooses service providers, which can be either commercial or non-commercial banks. The model is developed using a neural network (NN) with a history data of 2973 records and comprising four sub-models. The model is improved by varying the NN structure and EPOC. The best model provides an accuracy rate of 100%. The second model is the approval model, comprising 55 input factors for predicting the result of loan requests, which can determine if the loan should be approved with the full amount of the request, approved with a lesser amount or another outcome. The model is developed using a neural network with history data of 787 records. This model is composed of three sub-models; the best model of which gives an accuracy rate of 55%. The third model is the hybrid decision model, linking preference factors and approval factors with external factors. The model is constructed using system dynamics factors, approval factors, financial institutions and system dynamic modeling and the model can simulate the result if the input is changed.https://www.mdpi.com/2076-3417/12/22/11730neural networkpreference factorsapproval factorsfinancial institutionssystem dynamic |
| spellingShingle | Nachapong Jiamahasap Sakgasem Ramingwong Hybrid Decision Models of Leasing Business for Thailand Using Neural Network Applied Sciences neural network preference factors approval factors financial institutions system dynamic |
| title | Hybrid Decision Models of Leasing Business for Thailand Using Neural Network |
| title_full | Hybrid Decision Models of Leasing Business for Thailand Using Neural Network |
| title_fullStr | Hybrid Decision Models of Leasing Business for Thailand Using Neural Network |
| title_full_unstemmed | Hybrid Decision Models of Leasing Business for Thailand Using Neural Network |
| title_short | Hybrid Decision Models of Leasing Business for Thailand Using Neural Network |
| title_sort | hybrid decision models of leasing business for thailand using neural network |
| topic | neural network preference factors approval factors financial institutions system dynamic |
| url | https://www.mdpi.com/2076-3417/12/22/11730 |
| work_keys_str_mv | AT nachapongjiamahasap hybriddecisionmodelsofleasingbusinessforthailandusingneuralnetwork AT sakgasemramingwong hybriddecisionmodelsofleasingbusinessforthailandusingneuralnetwork |