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研究生:林君宜
研究生(外文):Lin, Chun-Yi
論文名稱:運用M5'模式樹分析放款發生逾期因子
論文名稱(外文):Using the M5' Model Trees for Analyzing Causes of Non-Performing Loan
指導教授:利德江利德江引用關係
指導教授(外文):Li, Der-Chiang
口試委員:林耀三戴文禮葉俊吾
口試委員(外文):Yao-San LinWen-Li DaiChun-Wu Yeh
口試日期:2015-01-19
學位類別:碩士
校院名稱:國立成功大學
系所名稱:高階管理碩士在職專班(EMBA)
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:57
中文關鍵詞:逾期放款放款覆審決策樹
外文關鍵詞:Non-performing loanLoan reviewDecision tree
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放款向來是銀行主要的獲利來源之一,因此放款若是發生逾期,非但銀行原本預期的利息收入不能如期收取,若是透過處分擔保品或向借保人訴追無效,可能徒增代墊訴訟費用並血本無歸。「放款覆審」猶如人體定期健檢,可以提早發現放款逾期徵兆並加以預防。惟過去對於借款人會發生逾期之分析研究多著重於貸放前之落實徵信與審查防範,鮮少研究以貸放後之管理方式作為亡羊補牢的手段,所以本論文針對個案銀行遭遇的放款覆審問題,以該銀行自民國96年起至103年9月底止各月底所列報新發生之逾期授信戶,依本研究擷選逾期放款風險因子,組成資料分析研究。本研究列入的自變數為16項『逾期放款風險因子』,目標變數為『列報逾期距到期天數』,基於決策樹的概念,運用M5'模式樹之分析結果發現關鍵風險因子分別組成企業逾期放款與個人逾期放款分別有7條與8條規則式可供徵審部門作為修訂授信覆審之範圍頻率參考。不論是企業或個人借款列報逾期距離該筆放款到期天數都與放款科目的關聯性最大,所以個案銀行若加入「放款科目」作為覆審頻率之衡量標準,將可避免不必要的覆審人力成本浪費並同時提升覆審效果。
Abstract

SUMMARY
This study focuses on the loan review problems encountered by the case bank. Drawing upon this bank’s new overdue loan accounts reported at the end of each month from Jan 2007 to Sep 2014, this study attempts to capture the risk factors for non-performing loans (NPL). The independent variables are 16 “risk factors for NPL”, and the target variable is “the difference of the date of reporting NPL from the due date”. Based on the concept of decision tree, this study uses the M5' model trees to identify key risk factors and establish 7 and 8 rules for corporate NPLs and individual NPLs, respectively. The results can be a reference for the loan review department in adjusting the scope and frequency of credit reviews. From both corporate and individual loan accounts, it is found that “the difference of the date of reporting NPL from the due date” is most correlated with “loan type”. In other words, by adding “loan type” into the criteria for determining the review frequency, the case bank can save unnecessary labor cost and also improve the effectiveness of their review operation.

Keywords: Non-performing loan (NPL), loan review, decision tree, M5' model trees

INTRODUCTION
This study focuses on NPLs, a common problem encountered by banks. Factors of NPLs are numerous. Previous research of causes of NPLs has paid more attention to credit review before lending, and little research has explored the remedial measures for managing NPLs. Motivated to fill this gap, this study will investigate if the credit review scope and frequency required by the case bank are effective for detecting early signs of NPLs and further reducing occurrence of NPLs or they are so over-stressed that they have caused a burden to credit reviewers? By capturing the associations among the key risk factors for NPL from a dataset of overdue loan accounts, this study attempts to provide the credit review department of the case bank an effective approach to reduce NPLs.
METHOD
In order to analyze the case bank’s NPLs, this study employs the concept of decision tree to capture the key risk factors for corporate NPL and individual NPL and then develop a forecast model. This model will be compared with the current review rules, and overfitting will be avoided. The M5' model trees, an improved extension of CART decision trees, will be adopted for analysis. The M5' model trees involves five procedures, including tree-growing, tree-pruning, tree-smoothing, dealing with enumerated attributes, and dealing with missing values.
EXPERIMENTAL RESULTS
The following experiments are using regression trees, using the K-fold to show the experimental results, and the experiment was repeated for 30 times, Table 4-3 and Table 4-4 in parentheses represents the Standard Deviation after 30 times different experimental results . To calculate the difference between the valuation and the actual value of the estimated value and the actual value and the description of the dispersion of the sample, using the mean absolute error (MAE) and mean square error (RSME) compare two indicators to assess the quality of the model Collocation.
Based on the dataset of corporate NPLs, the following experiment results and model tree can be created:
Table 1. The experiment results of corporate NPLs
2-fold 3-fold 5-fold 10-fold
MAE 217.60(54.81) 192.11(48.36) 181.84(59.39) 174.33(78.57)
RMSE 376.18(122.81) 334.16(123.25) 307.06(137.33) 277.97(164.83)
Figure 1. The model tree for corporate NPLs
Based on the dataset of individual NPLs, the following experiment results and model tree can be created:
Table 2. The experiment results of individual NPLs
2-fold 3-fold 5-fold 10-fold
MAE 975.07(111.62) 895.64(97.41) 840.30(137.73) 801.25(188.13)
RMSE 1251.50(136.31) 1151.17(122.53) 1080.40(183.25) 1019.53(254.24)
Figure 2. The model tree for individual NPLs
CONCLUSION
In this study, the independent variables are 16 “risk factors for NPL”, and the target variable is “the difference of the date of reporting NPL from the due date”. The analysis with the M5' model trees reveals 6 key risk factors for corporate NPLs, including “loan type”, “amount secured by other accounts in the bank-1”, “collateral”, “total amount of loans from the bank”, and “the difference of the date of reporting NPL from the lending day” and 4 key risk factors for individual NPLs, including “loan type”, “the difference of the date of reporting NPL from the lending day”, “total amount of loans from the bank”, and “amount of cash lending”. Based on the above risk factors, this study develops 7 and 8 rules for corporate NPLs and individual NPLs, respectively, which can be an important reference for the bank’s loan review department.
From these factors and rules, it can be observed that particular attention should be paid to three types of corporate loans, including a52 (medium-term unsecured loan), a53 (medium-term secured loan), and a55 (long-term secured loan). As for individual loans, particular attention is required for a55 (long-term secured loan) with a loan duration shorter than 2,799 days (approximately 7.6 years).
The statistical classification method and consideration of more non-financial variables in the review of corporate NPLs can be improved in subsequent research.
目錄
摘要 I
誌謝 VI
目錄 VII
圖目錄 IX
表目錄 X
第一章 緒論 11
1.1 研究背景 11
1.1.1 銀行授信 12
1.1.2 授信覆審 13
1.2 研究動機 18
1.3 研究目的 20
1.4 研究範圍與限制 20
1.5 研究流程 22
第二章 文獻探討 24
2.1 逾期放款(Non-Performing Loan) 24
2.1.1公司逾期放款 25
2.1.2 個人逾期放款 28
2.2 決策樹(Decision tree)簡介 28
2.3文獻評析 32
第三章 研究方法 34
3.1 M5法模式樹 34
3.1.1 成長過程(Tree-growing procedure) 35
3.1.2 修剪過程(Tree-pruning procedure) 35
3.1.3 平滑化過程(Tree-smoothing procedure) 36
3.2 M5模式樹 36
3.2.1 名目屬性轉換(Ennumerated Attributes) 37
3.2.2 遺漏值處理(Dealing with missing values ) 37
3.3 研究工具 38
第四章 實證結果 39
4.1 逾期放款因子屬性值說明 41
4.2 實例驗證 46
4.2.1 企業戶逾期放款實例 46
4.2.2 個人戶逾期放款實例 47
4.3 實驗結果說明 48
第五章 結論與建議 52
5.1 結論 52
5.2 建議 52
參考文獻 54
圖目錄
圖1-1 銀行授信作業流程圖 13
圖1-2企業授信覆審頻率 16
圖1-3個人授信覆審頻率 17
圖1-4 放款發生逾期之部分風險因子 21
圖1-5 本研究流程圖 23
圖4-1 企業戶逾期放款試驗結果產生的模式樹 47
圖4-2 個人戶逾期放款試驗結果產生的模式樹 48
表目錄
表2-1 以企業財務變數分析作為預警模式之相關研究 25
表2-2 以企業非財務變數分析作為預警模式之相關研究 26
表2-3綜合財務與非財務變數分析作為預警模式之相關研究 27
表2-4 個人逾期放款因素之相關研究 28
表2-5 資料探勘技術之分類 29
表2-6 決策樹演算法之比較 33
表4-1 個人戶前30筆逾期放款資料 39
表4-2 企業戶前30筆逾期放款資料 40
表4-3 企業戶的實驗結果 46
表4-4 個人戶的實驗結果 47
表4-5 企業逾期放款試驗結果規則式 49
表4-6 個人逾期放款試驗結果規則式 50
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