Predicting the Credit Risk of Loans Using Data Mining Tools

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.

NILCHI MOSLEM; Moghadam Khashayar; NASER SADRABADI ALIREZA; FARHADIAN ALI

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

Journal Paper

Paper Information

Journal: Journal of Monetary and Banking Research Year:2019 | Volume:11 | Issue:38 Page(s): 625-654

Download Full-Text

Persian Verion

View:

527

Download:

Cites:

Information Journal Paper

Title

Predicting the Credit Risk of Loans Using Data Mining Tools

Author(s)

NILCHI MOSLEM | Moghadam Khashayar | NASER SADRABADI ALIREZA | FARHADIAN ALI | Issue Writer Certificate

Keywords

Not Registered.

Abstract

One of the most common causes or credit phenomenon that is taken into account for credit risk is the customer’ s noncompliance with the commitments. Thus, by predicting the behavior of loan applicants, the growth rate of debts can be decreased. Hence, this study is conducted on corporate applicants for loans in one of the public banks in Iran. In this paper, the main elements comprising the customers’ behavior are selected with the help of categorized sample collection of 521 random samples from all corporate applicants. the process of data preparation, then, is accomplished by summarization, integration, and interpolation of some lost data. In the next step, 85 key performance indicators are selected for modeling. In order to measure the importance degree of the affecting elements on the customers’ behavior, the decision tree، neural net algorithms and Support Vector Machine were applied, the decision tree algorithm with 14 percent average absolute error, having the highest degree, was recognized as the top algorithm capable of assessing the probability of defaults. Finally, based on the available data and according to the results of the CHAID decision tree, contract maturity, amount of interest, number of installments, operating profit to asset, type of contract, average debt in 3 months ago and loan amount are the most important indicators affecting the customers’ behavior. Taking these indicators into account, before granting the loans, could have a significant role in the prediction of the customers’ behavior and the related decision making.

Cites

No record.

References

No record.

Cite

APA: Copy

NILCHI, MOSLEM, Moghadam, Khashayar, NASER SADRABADI, ALIREZA, & FARHADIAN, ALI. (2019). Predicting the Credit Risk of Loans Using Data Mining Tools. JOURNAL OF MONETARY AND BANKING RESEARCHES, 11(38 ), 625-654. SID. https://sid.ir/paper/364580/en

Vancouver: Copy

NILCHI MOSLEM, Moghadam Khashayar, NASER SADRABADI ALIREZA, FARHADIAN ALI. Predicting the Credit Risk of Loans Using Data Mining Tools. JOURNAL OF MONETARY AND BANKING RESEARCHES[Internet]. 2019;11(38 ):625-654. Available from: https://sid.ir/paper/364580/en

IEEE: Copy

MOSLEM NILCHI, Khashayar Moghadam, ALIREZA NASER SADRABADI, and ALI FARHADIAN, “Predicting the Credit Risk of Loans Using Data Mining Tools,” JOURNAL OF MONETARY AND BANKING RESEARCHES, vol. 11, no. 38 , pp. 625–654, 2019, [Online]. Available: https://sid.ir/paper/364580/en

Related Journal Papers