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研究生:高大福
研究生(外文):Ta-Fu Kao
論文名稱:資料探勘技術於金融債權管理之應用
論文名稱(外文):A Data Mining Application of Non-Performance Loans
指導教授:蔡佳勝蔡佳勝引用關係
指導教授(外文):Chia-Sheng Tsai
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:78
中文關鍵詞:類神經網路決策樹不良債權資料探勘
外文關鍵詞:non-performance loansdata miningdecision treeneural network
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近年來,許多經濟問題的其中一個項目是不良債權。不良債權的減少在經濟層面上是減少金融逾放比率;而在資產管理公司方面,不良債權則是創造營收的機會。雖然資產管理公司可依賴不斷釋出的財務政策,據以進行債務的催收,但債務人的繳款狀況却不容易掌握,因為不良債權的發生並非偶然,債務人多是無力償還的族群,因此我們研究的目的是找出債務人償債的機會。
由於資料探勘技術是業界發掘商業知識的方法,債務催收管理同樣也可使用資料探勘技術:決策樹與類神經網路,來達到人事及成本的節省,因此更正確的決策判斷以增進效率為我們研究的目的及目標。
Resent years, one of the economic problems is the non-performance loans. The decrease of non-performance loans, in economic, are to decrease the overdue loans of finance; in asset management companies, the non-performance loans are the opportunities to create revenue. Although the asset management companies can rely on frequently released financial policy, and applying to the collection case, but the status of contributions to the debtor is not easy to grasp, because the emergence of non-performance loans is not accidental, the debtors are the groups of unable to repay. So it is the purpose we need to find out the opportunity of debtor’s willing to repay. As a result of data mining is the way of industry to find business intelligent, the collection management of the business can also apply with data mining technologies: decision tree and neural network, to achieve cost and personnel savings, so that the direction of a more accurate decision-making, even more increase the efficiency, which is the research purpose and ultimate goal of this paper.
CHINESE ABSTRACT i
ENGLISH ABSTRACT ii
TABLE OF CONTENTS iii
LIST OF FIGURES iv
LIST OF TABLES v
CHAPTER 1 PREFACE 1
1.1 Research Background 1
1.2 Study Motives 3
1.3 Research Purposes 5
1.4 Research Framework 6
1.5 Research Steps 7
CHAPTER 2 LITERATURE REVIEW 10
2.1 The Definitions of Non-Performance Loans 10
2.2 The Handling of NPLs 13
2.3 Asset Management Companies 16
2.4 Data Mining Techniques 18
CHAPTER 3 RESEARCH METHODS 35
3.1 Introduction Research Tool 35
3.2 Research Content 43
CHAPTER 4 IMPLIMENTATION 51
4.1 Arrangement of Data Source 51
4.2 Cutting and Preparation of Data 61
4.3 Modeling and Assessment 66
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 74
5.1 Research Contribution 74
5.2 Recommendation 75
5.3 Future Direction 76
BIBLIOGRAPHY 77
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