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    題名: Determinant factors of non-performing loans of credit cards in Taiwan : an example from a commercial C bank
    其他題名: 台灣信用卡逾期放款的決定因子: 以C商業銀行為例
    臺灣信用卡逾期放款的決定因子: 以C商業銀行為例
    作者: 余支萬;Yu, Chih-wan
    貢獻者: 淡江大學國際商學碩士在職專班
    鮑世亨;Pao, Shih-hen;蔡政言;Tsai, Jeng-yan
    關鍵詞: 信用卡;徵審;風險管理;風險變數因子;逾期放款;羅吉斯迴歸模型;NPLs;Risk management;Credit card;Credit risk;Credit investigation;Logistic Regression Model
    日期: 2007
    上傳時間: 2010-01-11 01:21:58 (UTC+8)
    摘要: 本研究旨在分析探討銀行信用卡逾放風險的決定因子。因為近年來台灣銀行業的信用卡發卡泛濫,各銀行力圖搶攻信用卡市場商機,冀求快速高獲利高報酬,不顧所謂的超然穩健的徵審制度,而風險控管或被忽略,或被曲解,或蓄意束之高閣,發卡銀行甚少澈底認真的去執行安控措施。換言之,許多銀行失敗於監控其風險管理。銀行抱持上述的心理態度,肆無忌憚的超量發行信用卡,逾放比率如影隨形,伴隨漫無節制發卡量節節攀升。各銀行極力爭相濫發信用卡數年後的結果,也養成了信用卡持卡人濫用其信用,特別是大多數年青族群,台灣也因此產生了「解放卡奴」的嚴重社會問題。
    因此,各發卡銀行應如何去除發卡風險?那些是造成逾期放款(NPLs)的決定因子?銀行應如何創造獲利而不帶來逾放(NPLs)?卡債風暴災難之後,這些都成發卡銀行的熱門話題。本研究嘗試提供一種較佳的方式來辨識逾期放款(NPLs)的決定因子。我們以台灣某 C 商業銀行的信用卡逾期放款(NPLs)為主要研究對象,資料的抽樣期間以該行2003~2005年的發卡量為標的,隨機抽取發卡申請書總計330件,其中229件為「正常戶」;101件為「逾期戶」。而且就信用卡申請書所載之借戶資料,及聯徵中心備查之申戶信用資料各取5個自變數共10個,運用羅吉斯迴歸模型建立發卡授信評量模式。我們的實證發現如下:
    二、輸入十四個相關重要風險變數進入羅吉斯迴歸模型 (Logistic
    Regression Model) 計算,結果發現上述七種風險變數對於信
    型(LR Model-8)監控七種風險變數因子,篩選優良申戶承作外,
    The main purpose of this research is trying to analyze and
    to explore the determinant factors of Non-Performing Loans
    (NPLs) of credit cards. Years lately, the Taiwanese banks
    massively and abusively issued credit cards attempting to
    seize the business opportunities of market share; they actively intended to gain their speedy incomes and high returns. In spite of what is so-called stable and independent
    "Credit Investigation System", the risk management was totally to be neglected or to be abandoned on purpose, too. The credit card issuer banks seldom implemented security measures seriously and thoroughly; In other words, those banks failed to supervise their risk management. The above attitudes, which resulted in issuing excessive numbers of credit cards, simultaneously it accompanied the numbers of ever-increasing credit cards with the increasing of NPLs ratio, which was going high. After issuer banks aggressively developed credit card business for years, the rampant issuance of credit card also has led to a widespread abuse
    of credit by the cardholder, especially for most of younger
    Many Taiwanese banks eventually fell into traps, they suffered from high NPLs severely, and it seriously incurred “Liberating Card Slave” problem in Taiwanese society. Lots of banks were close to bankruptcy due to poor risk management, it was almost to incur another financial crisis to Taiwan. Finally those problems were deeply concerned by Taiwanese Government and the policy makers. Later on, the Ministry of Finance took a supervisory action. The Government urged banks holding conservative and prudent attitudes to issue their credit cards. They need to enhance banks’ risk management, and they have to accelerate writing off the bad debts of credit cards as quickly as possible to improving their credit-card asset qualities as well. The international context,"Basel II Framework" was built by BCBS. It was set for capital requirements and to improve the capital framework''s sensitivity to the risks that banks face. Also the Speech of Chairman Alan Greenspan emphasized that banks should be more on the overall structure and operation of risk-management systems. Bank’s loans are the largest and most obvious source of credit risk; however, banks should have a keen awareness to identify, measure, monitor, and control credit risk
    Hence, banks how to remove credit risk when they issue credit cards? Which are the Determinant factors of NPLs of Credit Cards? Banks how to make profit without NPLs? After the disaster of card-debt storm, these became the hot issues among the cards issuer banks. This research tries to offer a better way to recognize determinant factors of credit-card NPLs. We focused on credit-card NPLs of Commercial C Bank in Taiwan. The period of sampled data was from year 2003 to 2005 of issued credit cards. And we randomly sampled the data 330 cases in total, we found that 209 cases were “normal accounts”, and 101 cases were “overdue accounts”. Furthermore, we selected 10 independent variables from application forms and the personal credit standing records at JCIC. Applying Logistic Regression Model to construct a card issuing Evaluation Model, We found the evidences as follows:
    1.The analysis of variances reveals the factors in LR Model
    with a significant risk, such as the following 7 variables:
    Age, Education (high school), Monthly income, Unsecured
    loans to monthly income rate, Payment behavior (minimum
    payment), Frequency of inquiry, and Holding credit cards.
    2.The above related determinant factors of NPLs have a
    significant interaction to the occurrence of overdue
    accounts, hence the 7 factors chiefly concerned with a
    credit card issuer bank will gain profit or gain loss on
    its credit card business. We recommend the banks to
    supervise, identify, and control them severely.
    3.The “LR Model-8” of this thesis differ from the other
    relevant studies, we suggest specifically that if the
    banks wish to prevent NPLs efficiently; before a card being
    approved the banks need to use “LR Model-8” to control
    determinant factors of NPLs and having a sound “Risk
    Management” system, none of them can be neglected,
    otherwise the NPLs will ruin the card-issuer banks again
    sooner or later.
    顯示於類別:[國際企業學系暨研究所] 學位論文


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