臺灣博碩士論文加值系統

台灣自1992年開放新銀行設立後，銀行間之競爭日益激烈，復於1997年7月遭受亞洲金融風暴的侵襲，加上企業紛紛赴大陸投資，債留台灣情形相當嚴重。此外直接金融之興起，更使銀行業面臨存放利差逐年縮小、淨值報酬率下降的問題。鑑於銀行資金寬鬆且對企業授信採取保守的情況下，各金融同業乃大力拓展消費者貸款業務，並採取價格競爭手段，吸攬同業客戶，消費性房屋貸款業務儼然已成各銀行當今全力衝刺的重點授信項目。然因過度競爭，放款又流於浮濫，為確保授信之安全性，將違約風險降至最低，應為銀行業當務之急。
本研究以國內某一民營銀行之消費性房屋貸款案件為研究對象，以隨機取樣方式在其中部分行中，抽取1991年至2006年為其選取母體期間之樣本案件，母體案件數共計1153件，樣本案件截止2006年5月底止，計正常戶231筆，違約戶44筆，合計275筆，有效樣本252筆。
分析銀行業在房屋貸款授信模式中，必須考慮之風險評估因素，並依據銀行授信5P原則及其他考量因素，本研究採用：每元剩餘債務比率、借款人標準化所得、教育程度、景氣燈號指標、利率、保證人人數等六項變數。應用倒傳遞類神經網路模式，以202筆樣本進行訓練，再以50筆樣本進行測試，測試結果正常戶預測失敗數為0個，逾期戶預測失敗數為1個，整體預測失敗率為2%，整體而言精確度達98%。最後，隨機抽取17筆新貸案件樣本進行模擬預警測試，其中有5筆貸款案件之預測值小於0.2，顯示本研究發展出之倒傳遞類神經網路最適模式，預測此5筆貸款案件未來將發生違約情形，其餘12筆為正常案件，此預測結果之精確率將留待未來印證，以了解本研究發展出之最適模式是否可做為未來銀行承做房屋貸款業務時，准駁與否之有效參考。

Ever since 1992 when Taiwan government opened up restrictions on the establishment of new banks in Taiwan, the competition among banks were getting more and more intensive. Furthermore, when Tiawan’s financial market was attacked by the Asia Financial Crisis in July 1997 and also lots of industries were attracted to China because of the China investment heat, it became a quite serious problem when more and more industries invested in oversea market and left their debts in Taiwan. In addition, arising of direct banking also forced banks to face problems of gradual diminution in interest rate margin in-between deposits and loans, and also decreasing ROEs. To reflect the situation of loosen bank funds but with conservative corporate banking credit granting policy, all in banking industries therefore exerted great efforts to expand consumers loan business; they also solicited customers in the same business with price competition strategy. At present consuming housing loan became a key credit-granting event that banks were dashing at with full strength. However, because of over-competition and excess loaning, ensuring credit granting security and minimizing default risks should be the most urgent and important issue that banks should work on at this moment.

Study subjects of this research were drawn from real cases of consuming housing loans in a private-owned bank in Taiwan. Sample cases were randomly selected from the total population during 1991 to 2006. The total population was 1,153 cases and the sample cases till the end of May 2006 were totaled 275 cases, in which there were 231 regular accounts, 44 default account and 252 valid accounts.

After analyzing the risk evaluation factors during housing loan credit-granting procedures and considering the 5P principles and other factors, this research adopted six variances, including the residual debt ratio for each dollar amount, borrower’s standardized income, borrower’s educational background, business indicators, interest rate and numbers of guarantors. The Back-Propagation Neural Network Model was applied to 202 samples for training purpose. Then 50 samples were drawn to do the test. Test result showed that number of estimated failure in regular account group was zero, number of estimated failure in overdue account group was 1 and the estimated overall failure percentage was 2%. In general, there achieved 98% accuracy. At last, 17 new loaning cases were randomly drawn to perform the simulated early warning test. Among the 17 cases, there were five loaning cases having estimated values that were under 0.2, indicating the most appropriate model in the Back-Propagation Neural Network Model that was derived from this research foresaw such five loaning cases in the future will be default cases and the remaining twelve cases were regular cases. The accuracy of the forecast will be tested and verified in the future, so that we can know whether this the most appropriate model that was derived from this research can be an effective reference tool when banks are determining housing loan business cases.

第一章 緒論••••••••••••••••••••••••••••••••••1
第一節 研究背景與動機•••••••••••••••••••••••••2
第二節 研究目的••••••••••••••••••••••••••••••4
第三節 研究範圍••••••••••••••••••••••••••••••5
第四節 研究結構流程••••••••••••••••••••••••••6
第二章 相關文獻回顧••••••••••••••••••••••••••8
第一節 造成債信不良因素之探討•••••••••••••••••8
第二節 探討消費性房屋貸款違約風險之貢獻••••••••14
第三章 評估理論內涵••••••••••••••••••••••••••23
第一節 類神經網路評估因素分析•••••••••••••••••23
第二節 倒傳遞類神經網路方法推演•••••••••••••••28
第四章 實證結果與模型預警••••••••••••••••••••42
第一節 實證結果說明•••••••••••••••••••••••••42
第二節 模型預警•••••••••••••••••••••••••••••55
第五章　結論與建議••••••••••••••••••••••••••56
第一節　研究結論••••••••••••••••••••••••••••56
第二節　建議••••••••••••••••••••••••••••••••57
參考文獻•••••••••••••••••••••••••••••••••••59
附錄一 Pythia 簡易操作手冊•••••••••••••••••••65

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