消費性信用貸款授信評量模式之研究;The Research on Evaluating the Risk of Consumer Loans

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题名:	消費性信用貸款授信評量模式之研究;The Research on Evaluating the Risk of Consumer Loans
作者:	莊士玄;Chuang,Shih-hsuan
贡献者:	產業經濟研究所碩士在職專班
关键词:	Logit迴歸;Probit迴歸;違約機率;鑑別分析;discriminate analys;logistic regression analysis
日期:	2009-07-22
上传时间:	2014-05-08 15:16:04 (UTC+8)
出版者:	國立中央大學
摘要:	銀行業對於貸放後之管理歷來並未加以重視，致使傳統銀行管理中對於核貸後之客戶僅有違約戶之催收機制，而缺乏覆核管理、還款行為分析等機制，在現今銀行業面臨獲利不穩、市場競爭激烈之環境下，各家銀行除不斷向外開拓客源以提升盈餘成長外，亦應掌握內部獨有之大量客戶資訊，進行核貸後之客戶管理、開發與應用，尤其在銀行業務中佔極重要貢獻之消費者貸款，在雙卡風暴衝擊之下，主管機關要求各家銀行對於核貸戶建立覆核機制，更突顯出貸放後管理之刻不容緩。惟消費者貸款業務具有金額小、筆數多等特性，銀行業如何在最低成本與最高效率之衡量下，達成主管機關之要求，並提升風險管理與業務發展之平衡，為現今銀行業者經營之首要課題。本研究以國內銀行為對象，針對影響其貸放後核貸戶違約之風險因子進行研究，並藉由Logistic迴歸、鑑別分析、Porbit迴歸等方法，建構出核貸戶之覆核模型，研究結果發現：學歷、負債所得比率、服務年資、信用卡及現金卡循環動用餘額、申貸者雙卡近一年遲繳次數、近三個月是否有他行查詢、借款年限等變數對核貸戶之違約程度影響顯著，其中學歷及借款年限變數應用於進件審查與覆核機制時，對於違約之影響獲取不同之結論。另本研究所建構之覆核模型，其總正確率分別為：Logistic迴歸分析為72.2%，鑑別分析為71.15%，Probit迴歸分析為72.2%，三者之間差異極小，且Logistic迴歸分析與Probit迴歸分析所獲之預測能力有相同的效果。經由本研究建立之覆核模型，所分別求出之個別核貸戶違約機率，可以正確掌握核貸戶之風險程度，於銀行業實務應用上，可使授信政策及風險控制得到適當之平衡，並於授信資產品質管理上真實反映出銀行所面臨之風險程度，使銀行之經營更形穩健，並符合法令之規範。 ;Having not yet considering much on consumer loan management, the banking set few functions on debt-collection for past due loan customers but lack such functions for review management and payment behavior analysis. Nowadays, the banking should know how to manage, explore and apply his unique internal information related to large credit underwriting customers under the circumstances of unstable profit and intensely competition, especially when the consumer loans contribute major profit for him. Because consumer loans have the characters of small amount and numerous accounts, the trade-off between risk management and profit development has become primary issue for banking under the consideration of lowest cost and highest efficiency. This research studies the risk factors of past due loan based on one commercial bank data. Three methods are used for this research. There are logistic regression analysis, discriminate analysis and probit regression analysis. The results shows that academic degree, loanratio, wordingyear, cardusesum, latetime, checknum, loanyear affected a lot on such kind of past due loan. Besides, by using above-mentioned methods to attest the accuracy of factors, the correct percentage of each model are closely, that are 72.2% of logistic regression analysis, 71.15% of discriminate analysis and 72.2% of probit regression analysis. The difference among these analyses is very little, but the logistic and probit regression analysis performs the same predict ability. The hit rate of contract violated ratio model could apply to the banking practical application to get the adequate balance between credit policy and risk control. The bank fully discloses the risk level of loan quality management in order to stabilize the performance and compliance the regulatory.
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