An artificial intelligence system for predicting customer default in e-commerce
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{VANNESCHI:2018:ESA,
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author = "Leonardo Vanneschi and David Micha Horn and
Mauro Castelli and Ales Popovic",
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title = "An artificial intelligence system for predicting
customer default in e-commerce",
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journal = "Expert Systems with Applications",
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volume = "104",
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pages = "1--21",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Risk
management, Credit scoring, Machine learning,
Optimization",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2018.03.025",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417418301702",
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abstract = "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 scoring (CS) is employed to identify
customers' 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
Services (RSS), which is currently one of the most used
systems to estimate customers' 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 the RSS main risk check. An
application of genetic programming (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 by RSS. The results show that GP
outperforms the generic CS model of the pre-risk check
in both classification accuracy and profit. GP achieved
competitive classificatory accuracy with several
state-of-the-art machine learning methods, such as
logistic regression, support vector machines and
boosted trees. Furthermore, the GP model can be used in
combination with the RSS main risk check to create a
model with even higher discriminatory power",
- }
Genetic Programming entries for
Leonardo Vanneschi
David Micha Horn
Mauro Castelli
Ales Popovic
Citations