Cite Article

Credit Card Detection System Based on Ridit Approach

Choose citation format

BibTeX

@article{IJASEIT1316,
   author = {Norbaiti Tukiman and Norhaiza Ahmad and Suhana Mohamed and Zarith Sofiah Othman and CT Munirah Niesha Mohd Shafee and Zairi Ismael Rizman},
   title = {Credit Card Detection System Based on Ridit Approach},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {6},
   year = {2017},
   pages = {2071--2077},
   keywords = {fraud; RIDIT; score; system; approach},
   abstract = {Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1316},
   doi = {10.18517/ijaseit.7.6.1316}
}

EndNote

%A Tukiman, Norbaiti
%A Ahmad, Norhaiza
%A Mohamed, Suhana
%A Othman, Zarith Sofiah
%A Mohd Shafee, CT Munirah Niesha
%A Rizman, Zairi Ismael
%D 2017
%T Credit Card Detection System Based on Ridit Approach
%B 2017
%9 fraud; RIDIT; score; system; approach
%! Credit Card Detection System Based on Ridit Approach
%K fraud; RIDIT; score; system; approach
%X Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1316
%R doi:10.18517/ijaseit.7.6.1316
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 6
%@ 2088-5334

IEEE

Norbaiti Tukiman,Norhaiza Ahmad,Suhana Mohamed,Zarith Sofiah Othman,CT Munirah Niesha Mohd Shafee and Zairi Ismael Rizman,"Credit Card Detection System Based on Ridit Approach," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, pp. 2071-2077, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.6.1316.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Tukiman, Norbaiti
AU  - Ahmad, Norhaiza
AU  - Mohamed, Suhana
AU  - Othman, Zarith Sofiah
AU  - Mohd Shafee, CT Munirah Niesha
AU  - Rizman, Zairi Ismael
PY  - 2017
TI  - Credit Card Detection System Based on Ridit Approach
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 6
Y2  - 2017
SP  - 2071
EP  - 2077
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - fraud; RIDIT; score; system; approach
N2  - Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1316
DO  - 10.18517/ijaseit.7.6.1316

RefWorks

RT Journal Article
ID 1316
A1 Tukiman, Norbaiti
A1 Ahmad, Norhaiza
A1 Mohamed, Suhana
A1 Othman, Zarith Sofiah
A1 Mohd Shafee, CT Munirah Niesha
A1 Rizman, Zairi Ismael
T1 Credit Card Detection System Based on Ridit Approach
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 6
YR 2017
SP 2071
OP 2077
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 fraud; RIDIT; score; system; approach
AB Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1316
DO  - 10.18517/ijaseit.7.6.1316