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

Sacide Kalayci, Mustafa Kamasak, Secil Arslan

Bilgisayar Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

10 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Credit risk analysis using machine learning algorithms
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404353
Yayın durumu	Yayınlandı - 5 Tem 2018
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Süre: 2 May 2018 → 5 May 2018

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

Gradient boosting
Neural network
Random forest
SME credit risk analysis
Stacked generalization
Support vector machines

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

10.1109/SIU.2018.8404353

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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,

day = "5",

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",

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

address = "United States",

}

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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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

UR - http://www.scopus.com/inward/record.url?scp=85050818475&partnerID=8YFLogxK

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap