Discovering credit cardholder's behavior by multiple criteria linear programming
type: Item
instance of: scholarly article
title: Discovering credit cardholder's behavior by multiple criteria linear programming
zbMATH Open document ID: 1112.90350
author: Gang Kou
author: Yi Peng
author: Shi Yong
author: Morgan Wise
author: Weixuan Xu
published in: Annals of Operations Research
publication date: 2006-03-10T00:00:00Z
review text: In credit card portfolio management, predicting the cardholder's spending behavior is a key to reduce the risk of bankruptcy. Given a set of attributes for major aspects of credit cardholders and predefined classes for spending behaviors, this paper proposes a classification model by using multiple criteria linear programming to discover behavior patterns of credit cardholders. It shows a general classification model that can theoretically handle any class-size. Then, it focuses on a typical case where the cardholders' behaviors are predefined as four classes. A dataset from a major US bank is used to demonstrate the applicability of the proposed method.
Mathematics Subject Classification ID: 90B50
Mathematics Subject Classification ID: 90C08
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 5011526
zbMATH Keywords: data mining
zbMATH Keywords: classification
zbMATH Keywords: multi-criteria linear programming
zbMATH Keywords: credit cardholders' behavior
zbMATH Keywords: SAS algorithms
MaRDI profile type: Publication
full work available at URL: s10479-005-6245-5
OpenAlex ID: W2140853997
cites work: Simple but powerful goal programming models for discriminant problems
cites work: Data mining. Concepts and techniques
cites work: Q3415780
cites work: Q4546432
DOI: 10.1007/S10479-005-6245-5
description: scientific article; zbMATH DE number 5011526