面對「數位金融」（Bank3.0）、「金融科技」（Financial Techbology, FinTech）等國際發展趨勢的浪潮，金融業須順應科技發展趨勢，推動金融業金融科技創新，以降低作業成本，提升效率及提供客戶更完善多元的金融服務。而本研究旨在探討當前就學貸款承貸銀行所面臨的問題，並進一歩探討承貸銀行是否可藉由建立創新的數位金融服務平台，以簡化借款學生申請就學貸款的手續。本研究使用質性研究方法，以三家主要負責就學貸款業務的銀行主管為訪談對象，研究工具為自製之「應用數位金融於就學貸款業務經營之研究訪談大綱」，透過訪談過程中與研究參與者的對話及分享工作經驗，歸納出就學貸款導入數位金融服務的成功關鍵因素，以縮短銀行及學校的審核作業時間，降低作業成本，提升經濟效益。根據研究者多年銀行工作經驗與訪談資料分析結果，本研究歸納結論如下：一、發展就貸業務完全數位化；二、發展行動裝置數位化金融；三、數位金融兼顧便利與安全；四、提供政府與學校共享資源。而依據研究結果，本研究有以下建議：一、直接應用政府資訊縮短程序；二、放寬線上業務範圍提升服務；三、鬆綁法規發展多元安控機制；四、應用大數據為制度修訂參考。

本研究之目的主要是為探討國立政治大學大學部學生家庭社經背景對其工讀行為之影響。對於本研究的進行，研究者將探討「家庭社經地位」、「其他背景變項」、「助學貸款」與「工讀行為」等變項間的關係。 本研究以九十七學年度第二學期社會科學學院二至四年級大學部學生為調查對象，以問卷調查法蒐集資料，有效問卷434份。研究工具為自訂「政治大學社會科學學院大學部學生家庭社經背景調查問卷」，所取得之資料以Probit模型檢定，使用Limdep 7.0套裝軟體進行資料分析。 在敘述統計部分，樣本資料中，以社會系學生有工讀行為之比例最高，而財政系學生工讀之比例則相對最低；若以年級區分，三年級學生有工讀行為之比例遠高於二年級與四年級學生；在性別上，女生工讀之比例高於男性。經進一步分析，父親教育程度為專科或高中職者，該大學生打工比例占樣本數八成，母親教育程度在國中小以下者，該大學生打工比率最高，占86.89％；父親目前失業或退休者，其子女打工比率最高，為81.54％，而母親則不論是家管、工作中、失業或退休，大學生工讀行為占樣本比例，均高達7成。家庭月收入在3萬以下者，大學生工讀比例為90.20％，在家庭結構上，單親家庭之大學生工讀比例略高於雙親家庭，而手足總數超過4人者，工讀比例均為100％。申請助學貸款者，工讀比例為93.62％，調查並發現，有75.12％的樣本曾有工讀情形；依據曾打工之樣本進一步分析，在打工地點上，校內與校外所占比例相當，在對學業成績之影響上，60.74％認為無影響，持正面影響與負面影響之樣本，均近2成。 經由Probit模型估計後發現：在家庭社經地位中，父親教育程度為專科或高中職者，對大學生工讀行為有顯著的正向影響；母親教育程度則除了大學之外，博碩士、專科與國中小者均對大學生工讀行為有顯著的正向影響；父母親就業現況對大學生工讀行為無顯著影響；而家庭月收入在3萬元以下者，對大學生工讀行為則有顯著正向影響。 其他背景變項中，社會科學學院大學部各系所中，僅民族系對學生工讀行為有顯著的正向影響；而二年級對工讀行為有顯著的負向影響；性別對於工讀行為，在統計上並無顯著影響，手足總數在對大學生工讀行為上也呈現顯著的正向影響。 而在助學貸款方面，經過分析亦發現，對大學生的工讀行為有顯著的正向影響。 / The purpose of this study is to investigate the impact of family social and economic background of undergraduate students from National Taiwan Chengchi on their working decisions. For the proceed of this study, the researcher will probe about the relationship among the variables of “family social and economic background, “other background variables,” “student loan,” and “working decisions.” This study has based on the undergraduate students from sophomore to senior year with the College of Social Science at the second semester of 2008 academic year as its target, and questionnaire method is used to collection information, with 434 valid questionnaires retrieved. The research tool is of the self-produced “Family Social and Economic Background Questionnaire of Undergraduate Students from College of Social Science of National Chengchi University,” and the information obtained will be verified with Probit model, using Limped 7.0, software to conduct information analysis. In terms of descriptive statistics, it is found, from samples information, that students from department of sociology are given the highest ratio of working decisions, while those from department of finance are found with the lowest ratio. If it is classified in terms of year, the ratio of junior students with part-timing behavior is found far higher than those of sophomore and senior year; in terms of gender, the ratio of female students with part-timing behavior is relatively higher than those of male students. With further analysis, it is found that if the students whose fathers are found with educational background of junior college or high school the ratio of those students with part-timing behavior would have amounted to 80% of the samples, and if those students whose mother are found with educational background below junior high and elementary school the ratio of those students with part-timing behavior accounts the highest, amounting to 86.89%. As for father who are currently retired or unemployed, the ratio of their children is found with the highest part-timing behavior, amounting to 81. 54%; as for those students whose mother works as housewife, or who are with employment, unemployed, or retired, the ratio of university students who are part-timing among the samples has average reached 70%. For family with income less than NT$30,000, the ratio of student with part-timing behavior reaches 90.02%; in terms of family structure, the ratio of university student from single-parent family who part-times is slightly higher than those from dual-parent family; as for family with siblings over 4 people, the ratio of part-timing behavior is entirely 100%. For those students who apply for student loan, their ratio of part-timing behavior is 93.62%, and it is fond from the investigation that 75.12% of the samples are given with part-timing behavior; based on the samples with part-timing behavior for further analysis, it is found that the ratio of part-timing location within and outside the school is relatively the same; as for effect on their academic performance, 60.74% of the students believe that it does not effect at all, with about 20% of the samples that hold either positive and negative effect. It is found from the estimation of Probit model that if the educational background of the student’s father is found either with junior college or high school it would, in terms of family social economic status, render positive impact on the part-timing behavior of the student. Aside from educational background of the student’s mother who is found with college education, their educational background with either Ph.D, master, junior college, junior high, or elementary school, it would render with prominent positive impact on the part-timing behavior of students. As for current employment of the parents, it would hardly render any significant on the part-timing behavior of the student; for family whose monthly income that is less than NT$30,000, it would render with positive impact on the part-timing behavior of the student. Among other family variables, it is found that only the Department of Ethnology renders with positive impact on the part-timing behavior of students among each of the undergraduate students from the College of Social Science; sophomore year renders with significant negative impact on the part-timing behavior of the students. As for gender on part-timing behavior, it does not provide any significant impact statistically, while the number of total siblings has exerted prominent positive impact on the part-timing behavior of the students. In terms of student loan, it is found, after analysis, that it would exert rather significant and positive impact on part-timing behavior.

民國九十七年二月底，就學貸款的總申貸戶已突破九十萬人、總核貸金額也超過一千五百億台幣，雙雙創下歷史的新高；但在同時，學生逾期未繳的呆帳也成長到四十四億的台幣，逾放比相較一般銀行的2%，更是高達4.59%，中央銀行更認為其逾放比高達16%，這造成政府在財政上的龐大的負擔。 反觀近幾年被熱烈討論的微型貸款，其在貸款上的創新、和就學貸款同樣具有社會服務性質，將貸款提供給予貧困、一般商業銀行不願意接受的民眾，但又可以維持高度獨立的財務支撐性而為人稱奇。1970年代開始發展的微型貸款，其首位推行者Yunas於2006年時受到諾貝爾和平獎的肯定，其在孟加拉創辦的Grameen Bank更是創下97~99%的還款率。 是什麼因素造成原本商業銀行不接受的微型貸款可以成功，而實行良久的就學貸款卻面臨貸款難以回收的問題？本文希望透過商品及消費者的角度、而非傳統社會福利的角度來審視微型貸款和就學貸款；利用過去的文獻及個案，以及訪談在台灣的微型貸款計畫－創業鳳凰微型貸款、嬌生彭婉如貸款，來比較分析貸款成功的關鍵因素，以給予就學貸款改進上的建議。 本文利用現今普遍被銀行採用由Paul H. Hunn提出的信用貸款5P原則：People貸款戶、Purpose貸款用途、Payment還款來源、Protection債權保障、Perspective貸款戶展望；以及由Jerome McCarthy提出、經Philip Kotler推廣的行銷4P：Product 產品規劃、Price定價、Promotion產品促銷、Place通路劃分；透過這4P及5P原則來建立架構、分析比較個案。 本研究經由訪談個案相關人員、以及過去國內外文獻資料，瞭解到成功的貸款必須要注意三個關鍵的要素。一、計畫的設計必須配合當地環境：貸款的回收是透過借款人創造正向的經濟循環產生的收入，所以必須要有一個適合發展的政治社會環境；二、篩選願意接受改變的借款人並賦權：給予貸款者適當的能力及資源以幫助他們成功，這必須透過挑選有意願改變的人、給予需要的資源，包括貸款及訓練、或是諮詢顧問；三、透過社群來建立社會支援系統：透過建立借款人間正式或非正式的社群網絡，可以幫助資訊的交流及人脈的建立，提高借款人成功的機會及貸款計畫影響持續的時間。