CUSTOMER CREDIT CLUSTERING FOR PRESENTING APPROPRIATE FACILITIES

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AFSAR AMIR; HOUSHDAR MAHJOUB RAHMAT; MINAIE BEHROZ

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Management Research in Iran Year:2014 | Volume:17 | Issue:4 Page(s): 1-24

Download Full-Text

Persian Verion

View:

1,956

Download:

Cites:

Information Journal Paper

Title

CUSTOMER CREDIT CLUSTERING FOR PRESENTING APPROPRIATE FACILITIES

Author(s)

AFSAR AMIR | HOUSHDAR MAHJOUB RAHMAT | MINAIE BEHROZ | Issue Writer Certificate

Keywords

CREDIT RISK MANAGEMENTQ2

CUSTOMER VALIDATIONQ2

RFMQ2

SOM NEURAL NETWORKQ2

Abstract

Credit institutions to provide variety of granted facilities to their customers, need to conduct comprehensive studies in order to know qualitative and quantitative aspects of their applicants. By this way, they can accomplish a complete evaluation of repay ability measure and calculate the refund facilities probability and finance services (generally called as validation).The purpose of this study was ranking the customer groups and specifying the best part of them until brokerage firms can do their credit allocation process mechanically. Here, after the preprocessing of the data, we converted them into RFM model. Then SOM NEURAL NETWORK as one of the clustering algorithms changed the customers into 10 clusters. Using the proposed model, the clusters were ranked. The top clusters were identified and facilities grant operations were done to the members of these clusters.Finally, three clusters 5, 1 and 7 were defines as top clusters, which are the target customers. Coefficient facilities granted to the top three clusters are 0.271, 0.173 and 0.556, respectively.

Cites

References

No record.

Cite

APA: Copy

AFSAR, AMIR, HOUSHDAR MAHJOUB, RAHMAT, & MINAIE, BEHROZ. (2014). CUSTOMER CREDIT CLUSTERING FOR PRESENTING APPROPRIATE FACILITIES. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), 17(4), 1-24. SID. https://sid.ir/paper/396910/en

Vancouver: Copy

AFSAR AMIR, HOUSHDAR MAHJOUB RAHMAT, MINAIE BEHROZ. CUSTOMER CREDIT CLUSTERING FOR PRESENTING APPROPRIATE FACILITIES. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES)[Internet]. 2014;17(4):1-24. Available from: https://sid.ir/paper/396910/en

IEEE: Copy

AMIR AFSAR, RAHMAT HOUSHDAR MAHJOUB, and BEHROZ MINAIE, “CUSTOMER CREDIT CLUSTERING FOR PRESENTING APPROPRIATE FACILITIES,” MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), vol. 17, no. 4, pp. 1–24, 2014, [Online]. Available: https://sid.ir/paper/396910/en

Related Journal Papers