Addressing remitting behavior using an ordinal classification approach

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

Article

Keywords:

Nominal classification, Ordinal Classification, Support Vector Machine, Remittances

Authors:

• Campoy-Muñoz, Pilar
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Expert Systems With Applications

Volume:

41

Number:

10

Pages:

4752-4761

Month:

August

ISSN:

0957-4174

BibTex:

@article{ESWAPilar2014, author = "Pilar Campoy-Mu{\~n}oz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The remittance market represents a great business opportunity for financial institutions given the increasing volume of these capital flows throughout the world. However, the corresponding business strategy could be costly and time consuming because immigrants do not respond to general media campaigns. In this paper, the remitting behavior of immigrants have been addressed by a classification approach that predicts the remittance levels sent by immigrants according to their individual characteristics, thereby identifying the most profitable customers within this group. To do so, five nominal and two ordinal classifiers were applied to an immigrant sample and their resulting performances were compared. The ordinal classifiers achieved the best results; The Support Vector Machine with Ordered Partitions (SVMOP) yielded the best model, providing information needed to draw remitting profiles that are useful for financial institutions. The Support Vector Machine with Explicit Constraints (SVOREX), however, achieved the second best results, and these results are presented graphically to study misclassified patterns in a natural and simple way. Thus, financial institutions can use this ordinal SVM-based approach as a tool to generate valuable information to develop their remittance business strategy.", awards = "JCR(2014): 2.240 Position: 12/81 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", comments = "JCR(2014): 2.240 Position: 12/81 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", doi = "10.1016/j.eswa.2014.01.036", issn = "0957-4174", journal = "Expert Systems With Applications", keywords = "Nominal classification, Ordinal Classification, Support Vector Machine, Remittances", month = "August", note = "JCR(2014): 2.240 Position: 12/81 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", number = "10", pages = "4752--4761", title = "{A}ddressing remitting behavior using an ordinal classification approach", url = "http://dx.doi.org/10.1016/j.eswa.2014.01.036", volume = "41", year = "2014", }

Note:

JCR(2014): 2.240 Position: 12/81 (Q1) Category: OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Abstract:

The remittance market represents a great business opportunity for financial institutions given the increasing volume of these capital flows throughout the world. However, the corresponding business strategy could be costly and time consuming because immigrants do not respond to general media campaigns. In this paper, the remitting behavior of immigrants have been addressed by a classification approach that predicts the remittance levels sent by immigrants according to their individual characteristics, thereby identifying the most profitable customers within this group. To do so, five nominal and two ordinal classifiers were applied to an immigrant sample and their resulting performances were compared. The ordinal classifiers achieved the best results; The Support Vector Machine with Ordered Partitions (SVMOP) yielded the best model, providing information needed to draw remitting profiles that are useful for financial institutions. The Support Vector Machine with Explicit Constraints (SVOREX), however, achieved the second best results, and these results are presented graphically to study misclassified patterns in a natural and simple way. Thus, financial institutions can use this ordinal SVM-based approach as a tool to generate valuable information to develop their remittance business strategy.

Comments:

JCR(2014): 2.240 Position: 12/81 (Q1) Category: OPERATIONS RESEARCH & MANAGEMENT SCIENCE