Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi

Sacide Kalayci; Mustafa Kamasak; Secil Arslan

doi:10.1109/SIU.2018.8404353

Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi

Translated title of the contribution: Credit risk analysis using machine learning algorithms

Sacide Kalayci, Mustafa Kamasak, Secil Arslan

Department of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

In credit risk analysis, besides assessing risk of credit applications, taking decision by foreseeing risk of active credit is very important to decrease risk of financial institutions. In Turkey, recent studies reveal that for financial institutions, risk of SME credits is higher than other credit types such as consumer and corporate. Therefore, this paper focuses on predicting SME customer status for period of six months by utilizing application scoring additional to customer behaviour features. By utilizing Random Forest, Neural Networks, Support Vector Machines and Gradient Boosting, performance comparison and also feature analysis for customer behaviour are conducted. Finally, conducted experiments show that utilizing Stacked Generalization methods has positive effect on performance of SME credit risk analysis.

Translated title of the contribution	Credit risk analysis using machine learning algorithms
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404353
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SIU.2018.8404353

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Kalayci, S., Kamasak, M., & Arslan, S. (2018). Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404353

@inproceedings{559c9ae2340b4d2aa068b9747908ed65,

title = "Makine {\"O}ǧrenme Y{\"o}ntemleri ile Kredi Risk Analizi",

abstract = "In credit risk analysis, besides assessing risk of credit applications, taking decision by foreseeing risk of active credit is very important to decrease risk of financial institutions. In Turkey, recent studies reveal that for financial institutions, risk of SME credits is higher than other credit types such as consumer and corporate. Therefore, this paper focuses on predicting SME customer status for period of six months by utilizing application scoring additional to customer behaviour features. By utilizing Random Forest, Neural Networks, Support Vector Machines and Gradient Boosting, performance comparison and also feature analysis for customer behaviour are conducted. Finally, conducted experiments show that utilizing Stacked Generalization methods has positive effect on performance of SME credit risk analysis.",

keywords = "Gradient boosting, Neural network, Random forest, SME credit risk analysis, Stacked generalization, Support vector machines",

author = "Sacide Kalayci and Mustafa Kamasak and Secil Arslan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

month = jul,

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doi = "10.1109/SIU.2018.8404353",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

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Kalayci, S, Kamasak, M & Arslan, S 2018, Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404353

Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi. / Kalayci, Sacide; Kamasak, Mustafa; Arslan, Secil.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi

AU - Kalayci, Sacide

AU - Kamasak, Mustafa

AU - Arslan, Secil

PY - 2018/7/5

Y1 - 2018/7/5

N2 - In credit risk analysis, besides assessing risk of credit applications, taking decision by foreseeing risk of active credit is very important to decrease risk of financial institutions. In Turkey, recent studies reveal that for financial institutions, risk of SME credits is higher than other credit types such as consumer and corporate. Therefore, this paper focuses on predicting SME customer status for period of six months by utilizing application scoring additional to customer behaviour features. By utilizing Random Forest, Neural Networks, Support Vector Machines and Gradient Boosting, performance comparison and also feature analysis for customer behaviour are conducted. Finally, conducted experiments show that utilizing Stacked Generalization methods has positive effect on performance of SME credit risk analysis.

AB - In credit risk analysis, besides assessing risk of credit applications, taking decision by foreseeing risk of active credit is very important to decrease risk of financial institutions. In Turkey, recent studies reveal that for financial institutions, risk of SME credits is higher than other credit types such as consumer and corporate. Therefore, this paper focuses on predicting SME customer status for period of six months by utilizing application scoring additional to customer behaviour features. By utilizing Random Forest, Neural Networks, Support Vector Machines and Gradient Boosting, performance comparison and also feature analysis for customer behaviour are conducted. Finally, conducted experiments show that utilizing Stacked Generalization methods has positive effect on performance of SME credit risk analysis.

KW - Gradient boosting

KW - Neural network

KW - Random forest

KW - SME credit risk analysis

KW - Stacked generalization

KW - Support vector machines

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U2 - 10.1109/SIU.2018.8404353

DO - 10.1109/SIU.2018.8404353

M3 - Konferans katkısı

AN - SCOPUS:85050818475

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

Makine Öǧrenme Yöntemleri ile Kredi Risk Analizi

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