Pregled bibliografske jedinice broj: 877201
Selection of Variables for Credit Risk Data Mining Models: Preliminary research
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: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2017. str. 1599-1604 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 877201 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
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 : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2017, 1599-1604
ISBN
978-953-233-093-9
Skup
MIPRO 2017 - 40 th Jubilee International Convention
Mjesto i datum
Opatija, Hrvatska, 22.05.2017. - 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
Projekti:
IP-2014-09-3729 - Procesna i poslovna intelilgencija za poslovnu izvrsnost (PROSPER) (Bosilj Vukšić, Vesna, HRZZ - 2014-09) ( CroRIS)
Ustanove:
Ekonomski fakultet, Zagreb
Profili:
Nataša Šarlija
(autor)
Božidar Jaković
(autor)
Jovana Zoroja
(autor)
Mirjana Pejić Bach
(autor)