金融業對於國家經濟發展向來扮演重要角色，其中銀行業的企業授信的角色相當重要，若銀行擁有的是不良債權，將可能讓銀行面臨被倒帳的結果，進而可能對作為銀行一大部分的資金來源的一般或機構投資人產生負面影響，故若能有效建構財務危機預警模型，將能避免上述情形的發生。
關於建構財務危機預警模型之文獻已汗牛充棟，本研究之差別在於以邏輯斯回歸、決策樹、支持向量機等三種機器學習方法建構模型，並以不進行抽樣，且不對財務比率進行歸納之方式，並運用Python程式之套件Scikit-Learn實作模型，最後加入另一個由銀行業界專家問卷進行特徵工程所獲得的模型和未加入問卷的模型進行比較，希望讓本研究建構之模型於預測效能上有不錯的表現。
根據本研究實證發現，未加入問卷的模型的效能表現皆非常不錯，然而由銀行業界專家問卷進行特徵工程所獲得的模型的效能表現和前者相比下降居多，而兩者皆為了正確判別出財務危機資料而錯殺了不少的財務正常資料。

Financial Industry has been an issue on country’s economic growth. Among the financial industry, corporate loan has played an important role. If the loan the bank has is a non-performing loan, the bank could be faced with no payback, which could have a bad impact on personal or institutional investors. So, if we could build an effective financial crisis model, the condition mentioned above could be prevented from happening.
There has been many research related to building financial crisis model. However, there are some differences between this one and such. First of all, we used three machine learning approaches, which include Logistic Regression, Decision Tree, Support Vector Machine, to build the models. Second, we did the research without sampling. Third, we didn’t make an induction to get specific variables for modeling. Fourth, we did modeling with Python’s Scikit-Learn package. And last, we designed a questionaire to get viewpoints from the professionals in the banking industry for us to do feature engineering to create another models, and compare the models with the ones without questionaire. We expect such difference could have good influence on the models’ performance.
According to the result of empirical analysis, all of the models without questionaire have good performance. However, the performance of most of the models with questionaire have fallen when compared to the performance of the models without questionaire. And, no matter with or without questionaire, all of the models sacrificed crisis-free coporations to generate better performance on detecting corporations with crisis.

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