| Summary: | 碩士 === 輔仁大學 === 管理學研究所 === 97 === In view of the fast and diversified developments on free trade financial market, the leasing company provided an alternative solution other than existing banking product. Leasing could be categorized as asset based finance: to fuel the corporate's capital expenditure. Once the obligor disregards the repayment to the leaser, it will therefore cause a loss. To efficiently manage the exposure and secure the pledged collateral, the leasing company needs to set up an appropriate credit scoring system.
The purpose of this study is to explore the performance of credit scoring models on one leasing company using discriminant analysis, logistic regression and artificial neural networks. To demonstrate the effectiveness of the proposed approach, the credit scoring tasks are performed on one leasing company using the cross validation approach. As the analytical results reveal, artificial neural networks outperforms other data mining techniques in terms of classification accuracy and hence provides an efficient alternatives in implementing credit scoring tasks of the leasing industry.
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