پیش بینی ورشکستگی شخصی توسط داده کاوی کارت اعتباری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|48261||2013||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 2, 1 February 2013, Pages 665–676
A personal bankruptcy prediction system running on credit card data is proposed. Personal bankruptcy, which usually results in significant losses to creditors, is a rapidly increasing yet little understood phenomenon. The most commonly used methods in personal bankruptcy prediction are credit scoring models. Some data mining models have also been investigated in this domain. Neither the scoring models nor the existing data mining methods adequately take sequence information in credit card data into account. In our system, sequence patterns, obtained by developing sequence mining techniques and applying them to credit card data from one major Canadian bank, are employed as main predictors. The mined sequence patterns, which we refer to as bankruptcy features, are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance.