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題 名 | 銀行授信決策應用類神經網路之研究--抵押貸款之實證研究=Decision Making of Mortgage Loan Approval using Artificial Neural Network Approach |
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作 者 | 杜慶麟; 張瑞芬; | 書刊名 | 模糊系統學刊 |
卷 期 | 4:1 1998.08[民87.08] |
頁 次 | 頁31-44 |
分類號 | 562.33 |
關鍵詞 | 抵押貸款; 類神經網路; 模糊集合; 授信決策; Mortgage loan; Artificial neural network; Fuzzy sets; Decision-making; |
語 文 | 中文(Chinese) |
中文摘要 | 貸款案件之審核評估是銀行業重要決策之一,傳統上,這項決策都是由有經驗之 資深放款人員來進行, 本研究希望經由類神經網路( Artificial Neural Network-ANN ) 的訓練學習,將資深授信人員或信用分析主管的經驗及判斷以模糊集合( Fuzzy sets )量 化, 進而轉化成 ANN 中 nets 結構的加權值,成為一具有授信決策經驗之類神經網路專家 系統,使每個抵押貸款案件得到較客觀、公平且一致的審核,以減少人為主觀因素而導致決 策錯誤及缺失。此方法以四百八十四筆房屋抵押貸款為訓練實例,又以另一百筆貸款個案為 測試範例,其結果比實際貸款之逾放件數(即比人為錯誤決策之件數)顯著減少,對抵押貸 款信用評估之自動決策或放款作業之合理監督有相當大的助益。 |
英文摘要 | Loan assessment is an important decision-making process of commercial banks. Conventionally, the load approval decision is made by senior and experienced staffs of the banks. This research applies Artificial Neural Network (ANN) approach to emulate the decision-making process of experienced mortgage loan assessors. The ANN is trained based on the previous assessors' judgment and learns to mimic their assessment skills. The variables used in the ANN model are quantified using fuzzy set theory to better represent loan assessment factors. Four hundred and eighty-four loan applications are used as examples to train the parameters of the net. Other 100 randomly picked loan applications are further applied as testing cases. The case study shows a better decision is made (i.e., less bad loans are approved) using the ANN approach, in comparison to the human decision. Further more, the system offers benefits of reducing decision errors in loan approvals and also offers a rational means to evaluate and monitor the consistency of loan approval operation. |
本系統中英文摘要資訊取自各篇刊載內容。