Verifying Financial Dimensions of Enterprise and Constructing a Credit Scoring Model using LISREL

• Main Authors: Lin, Lee-Cheng, 林麗甄
• Other Authors: Tong, Lee-Ing
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/65170003488278525157

博士 === 國立交通大學 === 工業工程與管理系所 === 98 === As the international finance has been developed fast, the risk that enterprises are facing now is more complex than before. Thus, banks and financial institutions must develop a credit scoring model to effectively predict default probability and assess borrower default risk. Banks or financial institutions can utilize the results of credit scoring model to devise appropriate loan strategies for borrowers to reduce risk or losses from improper loans. Previous studies on constructing a credit scoring model, the financial dimensions were generally extracted from financial ratio variables using Factor Analysis (FA) without considering various loan periods. These dimensions were used to construct a one-stage credit scoring model using statistical models or neural networks. However, the relationship between financial dimensions and financial ratio variables may vary according to various loan periods. If various loan periods are not taken into account when constructing the one-stage credit scoring model using financial dimensions extracted by FA, the predictive power of the model may not be high. In addition, because the cost of granting a loan to a defaulter is much larger than that of rejecting a non-defaulter, enhancing the accuracy rate of defaults cases is urgently important for banks or financial institutions. However, in practice, default cases are usually much less than non-default cases when constructing a credit scoring model. Thus, the effect on predicting default cases is usually much smaller than that of non-default cases when constructing the credit scoring model using the imbalanced data. Now, although some studies constructed the credit scoring model using a two-stage classification method to improve the problem of predicting default cases caused by imbalanced data. These studies claimed that the accuracy rate of the credit scoring model constructed using a two-stage classification method is higher than that using a one-stage classification method. However, using two-stage classification methods to construct the credit scoring model still can not effectively increase the accuracy rate of defaults cases. Thus, this study proposes methods of constructing the one-stage credit scoring model and the two-stage credit scoring model, respectively to overcome the problems mentioned above. In constructing a one-stage credit scoring model, linear structure relation (LISREL) is utilized to find proper financial ratio variables to construct financial dimensions for various loan periods. Then, the constructed financial dimensions and Cox model are utilized to construct a one-stage credit scoring model. In constructing a two-stage credit scoring model, this study constructing the two-stage credit scoring model is composed of two stages. The fist stage is using the constructed financial dimensions and Cox model to divide the borrowers into three classes: default, non-default and undetermined borrowers. In the second stage, the data in the undetermined class are utilized to construct a classification model using Support Vector Machine (SVM). Finally, this study using the financial data of borrowers from the small -and-median sized enterprises in Taiwan to demonstrate that the proposed one- stage credit scoring model and two- stage credit scoring model can reflect that when an enterprise borrower’s loan period is longer, the risk that a bank or a financial institution must face is higher. Also the proposed methods effectively enhance the accurate rates of both default and non-default cases.