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Pregled bibliografske jedinice broj: 877201

Selection of Variables for Credit Risk Data Mining Models: Preliminary research


Pejić Bach, Mirjana; Zoroja, Jovana; Jaković, Božidar; Šarlija, Nataša
Selection of Variables for Credit Risk Data Mining Models: Preliminary research // 40th jubilee international convention on information and communication technology, electronics and microelectronics / Biljanović, Petar (ur.).
Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2017. str. 1599-1604 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Selection of Variables for Credit Risk Data Mining Models: Preliminary research

Autori
Pejić Bach, Mirjana ; Zoroja, Jovana ; Jaković, Božidar ; Šarlija, Nataša

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
40th jubilee international convention on information and communication technology, electronics and microelectronics / Biljanović, Petar - Rijeka : Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2017, 1599-1604

ISBN
978-953-233-093-9

Skup
MIPRO 2017 - 40 th Jubilee International Convention

Mjesto i datum
Opatija, Hrvatska, 22-26.05.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
decision trees, credit risk, variable selection

Sažetak
Credit risk is related to the risk of the borrower that the lender will not be able to return their debt including interest. Numerous researches have been conducted in the area of credit risk, both using classical models such as Altman Z-score and using machine learning methodology. However, the research using the data from Croatian financial institutions is scarce, especially research focused on the selection of the demographic and/or behavior variables. In addition, it is important to develop robust models that estimate credit risk as accurately as possible. The goal of this research is to develop a data mining model for prediction of credit risk, using the data from Croatian financial institutions on defaulted clients (demographic and behavior data). Decision tree models are constructed for the prediction of credit risk. Different algorithms for the variable selection are evaluated based on the classification accuracy of the decision trees developed based on the selected variables. This work has been fully supported by the Croatian Science Foundation under the project “Process and Business Intelligence for Business Performance” - PROSPER (IP-2014-09-3729).

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekt / tema
IP-2014-09-3729

Ustanove
Ekonomski fakultet, Zagreb

Citiraj ovu publikaciju

Pejić Bach, Mirjana; Zoroja, Jovana; Jaković, Božidar; Šarlija, Nataša
Selection of Variables for Credit Risk Data Mining Models: Preliminary research // 40th jubilee international convention on information and communication technology, electronics and microelectronics / Biljanović, Petar (ur.).
Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2017. str. 1599-1604 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Pejić Bach, M., Zoroja, J., Jaković, B. & Šarlija, N. (2017) Selection of Variables for Credit Risk Data Mining Models: Preliminary research. U: Biljanović, P. (ur.)40th jubilee international convention on information and communication technology, electronics and microelectronics.
@article{article, editor = {Biljanovi\'{c}, P.}, year = {2017}, pages = {1599-1604}, keywords = {decision trees, credit risk, variable selection}, isbn = {978-953-233-093-9}, title = {Selection of Variables for Credit Risk Data Mining Models: Preliminary research}, keyword = {decision trees, credit risk, variable selection}, publisher = {Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO}, publisherplace = {Opatija, Hrvatska} }




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