Construction of Classified Model for Financial Institution Loan Potfolio by Soft Computing Method

• Main Authors: Wen-Yuan Chen, 陳文淵
• Other Authors: Shuo-Chang Tsai
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/52906521895935333038

碩士 === 亞洲大學 === 經營管理學系碩士在職專班 === 97 === Financial institution wants to make money by traditional loaning service is becoming more difficult in such difficult management environment. The huge non-performing loans are not reduce the earning capacity of the financial institution, but also influence the stability to the whole financial system. This study collect the financial institution loans portfolio classifies factors and analyzes its classified indicator. Utilize the soft computing method to build and construct the financial institution loans portfolio classified model and rebuild new one which is more exactly and more reliable classified model. To provide a reference resource of sale by sealed tender or negotiated price when financial institution treat their non-performing loans. Test the indicator susceptibility of the classified model finally, distinguish the key factor of classification. We select one large public bank in the middle in Taiwan, use its loaning assets appraisal table from 2002 to 2007 as our case study source. According to the 「non-performing loans measures」 rules made by Ministry of Finance , financial institutions evaluate the balance sheet and out of the balance sheet, except the performing loans and overdue loans within a month ( including a month) are classified type 1, the other non-performing loans should be classified into type 2—pay attention、 type3—maybe will regain、 type 4—difficult to regain、 type5—regain hopeless, by evaluating the creditor's rights guarantee situation and the exceeding the time limit time length. In this study, we sieve 360 non-performing loans cases and 90 normal loans cases from the really cases as the samples of the loans portfolio classified model. Before constructing and proving the building of the model, it must be divide the samples into training sample and testing sample to facilitate follow-up distinguishing loans portfolio classes correctly. Training samples are used for estimating the loans portfolio classes model of financial institution, testing samples are used for distinguishing the loans portfolio classes of financial institution. The real example result shows that the correct prediction rate of training sample only the networkⅠand networkⅢ are 100% on four kinds of network structure. As its RMSE number value, the network Ⅲ (regards Norm-Cum-Delta-Rule as the rule of studying, TanH as a transfer function) is the best one, RMSE is 0.1476, and the networkⅣ (regards Norm-Cum-Delta-Rule as the rule of studying, Sigmoid is the transfer function) its RMSE number value 0.1955 is the worst one. On the test sample side, the correct prediction rate only the networkⅠand networkⅢ are 94.67% on four kinds of network structure. As its RMSE number value, the network Ⅰ (regards Delta-Rule as the rule of studying, TanH as a transfer function) is the best one, RMSE is 0.2275, and the networkⅣ (regards Norm-Cum-Delta-Rule as the rule of studying, Sigmoid is the transfer function) its RMSE number value is the worst on 0.1955. That is to say, our research regarding Delta-Rule as the rule of studying and TanH as a transfer function get the best value of back-propagation neural network model. So we use the model to analyze the sensitivity, find that noticeable parameter of influence loans portfolio classes is the the exceeding the time limit time length, and the other parameter in sequence is the collateraling value, collaterals, the debtor, the type of collateral, and collaterals located. This result can be a reference to the member of financial institute to deal with practical cases for saving time and reducing exam time. And supply financial institute some references (the case exceeding the time limit period, the collateraling value, collaterals, the debtor, the type of collateral and collaterals located) to deal with non-performing loans (NPL) sale by sealed tender gives to asset management company (AMC), make sure they can combine the optimal asset portfolio composition of NPL and undertaking the collateral, to be the treatment of sale by sealed tender or negotiated price. This can help to attract more biders on the sale process, and enhance creditor's rights returns-ratio. At same time, the classified model also can strengthen the prediction system of loan assets management, to do preventive management work well.