یک سیستم هوش مصنوعی برای پیش بینی پیشفرض مشتری در تجارت الکترونیک
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|107570||2018||33 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 104, 15 August 2018, Pages 1-21
The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoringÂ (CS) is employed to identify customersâ default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution ServicesÂ (RSS), which is currently one of the most used systems to estimate customersâ default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with theÂ RSS main risk check. An application of genetic programmingÂ (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed byÂ RSS. The results show thatÂ GP outperforms the genericÂ CS model of the pre-risk check in both classification accuracy and profit. GPÂ achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, theÂ GPÂ model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power.