本研究旨在探討青年創業貸款申貸人貸款成功之關鍵因素，應用描述性統計、交叉分析、Logit模型分析(Logit Model)與區別分析(Discriminant Analysis)等方法，評估青年創業貸款申貸人於取得貸款後，是否為正常繳款做為貸款成功之依據。本研究選取之投入項為年齡、授信金額、從業經驗年數、貸款金額、現金卡/信用卡借款餘額、產業別、資產情況、學歷、婚姻、創業本業非本業、逾期還款記錄、營業場所自有或租用、性別、保證人。實證結果顯示：

一、Logit模型分析結果顯示，青年創業貸款成功之顯著變數有產業別、從業經驗年數、資產情況、學歷、營業場所自有租用等變數，其餘變數在迴歸式中並不顯著。所得之迴歸式對貸款成功的預測總正確率為83.3%，對逾期戶則為61.8%。
二、區別分析結果顯示從業經驗年數、資產情況、學歷等變數在區別函數中為顯著。其對正常戶之預測正確率為68.6%，逾期戶之預測正確率為73.5%。
三、本研究結果，對青年創業貸款是否成功之預測，Logit模型之正確率優於區別分析。
四、顯著變數，其重要性以資產情況最為重要。實務上，擁有資產之借款人，對銀行債權保障立場上，確實為最具可信的因素。

This study targets on the key success factors of youth entrepreneurship loan. Description analysis, crosstabs, Logit model analysis and discriminant analysis are applied to evaluate breach of youth entrepreneurship loan. The input variables are age of borrower, loan balance, career experience, loan size, loan balance for credit/cash card, industry, assets, education, marital status, intra or non-intra industry Entrepreneur, breach record, ownership of operation site, sex and vouchers. The results show that:
(1)From Logit model analysis, the significant variables are industry, career experience, assets, education and ownership of operation site. The forecast accuracy rate of regression model is 83.3% in total and 61.8% for bad loan.
(2)From discriminant analysis, the significant variables are career experience, assets, education. The forecast accuracy rates of discriminant function are 68.6% for the good loan and 73.5% for bad loan.
(3)The result of this study shows that Logit model analysis has better total forecast accuracy rate than discriminant analysis.
(4)The most important signification variable in the regression model is assets of borrower. In practice, asset of borrower is the most reliable factor for credit risk control of banks.

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