近年以來，由於新冠肺炎(Corona Virus Disease 2019，COVID-19)疫情與供應鏈斷鏈等因素，中古車交易量未減反增，市場規模與汽車貸款(車貸)業務承作量不斷攀升，違約數量與金額亦日益增加。對車貸業者而言，如能找出影響車貸違約之因素與決策規則，並進而管理違約風險，將可避免損失。
過去，多有學者針對信用評等問題提出決策分析之架構與演算法，唯少有論文探討影響二手車貸違約之因素，但對車貸業者而言，本問題極為重要。為解決此問題，本研究擬定義混合多準則決策模式，探勘車貸違約客戶資料庫，並推衍影響車貸違約之關鍵要素及決策規則。首先，本研究以優勢約略集合 (Dominance Based Rough Set Approach，DRSA) 探勘違約客戶之特徵，推衍核心屬性，推導決策規則後，以形式概念分析(Formal Concept Analysis)探勘影響車貸違約之主要概念，並以基於決策實驗室(Decision Making Trial and Evaluation Laboratory，DEMATEL) 之分析網路流程 (Analytic Network Process，ANP)或 DANP，以專家問卷發展核心屬性間之影響關係，及對應屬性之權重。實證研究結果得出之決策規則，可以作為汽車貸款公司核貸的參考。
本論文以北台灣主要車貸廠商之車貸違約客戶資料庫為探勘標的，實證研究架構。依據以優勢約略集合探勘資料之結果，客戶之年紀、性別、婚姻狀況、教育水準、收入與借款金額為核心屬性，並推衍「若客戶之學歷為大學以上，貸款金額高於150萬，則會違約」等十五條決策規則。另外，過去是否有違約紀錄、貸款金額與收入為影響違約主要因素。本研究之結果，可做為未來金融機構核貸之依據，亦可作為導入智能自動化核貸機制與發展智能車貸平台之用。

In recent years, the transactions of second hand cars have increased due to the Corona Virus Disease 2019 (COVID-19) epidemic, supply chain disruption and other factors. The number and amount of auto loans are increasing, and the number and amount of defaults are also increasing. For auto lenders, if they can find out the factors affecting auto loan defaults or manage these risk factors in the credit granting process to reduce loan defaults, many losses can be avoided.
Numerous algorithms and frameworks have been proposed by scholars to solve credit scoring problems in the past. Only a few studies have examined the factors affecting second car loan default. However, this issue is of great importance to the auto loan industry. Therefore, this study intends to define a hybrid multi-criteria decision making (MCDM) model to mine the database of defaulting customers of loans of second hand cars. First, this study introduces the Dominance Based Rough Set Approach (DRSA) to analyze the characteristics of the defaulting clients, derive the core attributes as well as the decision rules. Then, the Formal Concept Analysis (FCA) is adopted to derive the main concepts affecting the default of auto loans. After that, the Decision Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Process, or the DANP, is used to derive the influence relationships among the core attributes and the weights associated with these attributes. The empirical results can be used as a reference for auto loan companies.
Based on the database of one of major financial institutions in Taiwan, the feasibility of the analytic framework was verified. According to the mining results of the customer database, age, gender, marital status, education, income and loan amount are the core attributes, and 15 decision rules including "If those customers who own college degree or above and borrow more than 1.5 million dollars, then the customer will default" are derived. In addition, the record of violation, loan amount and income are the main factors affecting the default. The results of this study can be used as a basis for future loan verification by financial institutions, as well as for the introduction of intelligent automatic loan verification mechanism and the development of intelligent vehicle loan platform.

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