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master's thesis
Primjena logističke regresije pri procjeni rizičnosti korisnika kredita
Zagreb: University of Zagreb, Faculty of Economics and Business, 2019. urn:nbn:hr:148:598360
Bojčić, Katja
University of Zagreb
Faculty of Economics and Business
Department of Statistics

Institutional repository: REPEFZG

Cite this document

Bojčić, K. (2019). Primjena logističke regresije pri procjeni rizičnosti korisnika kredita (Master's thesis). Zagreb: University of Zagreb, Faculty of Economics and Business. Retrieved from https://urn.nsk.hr/urn:nbn:hr:148:598360

Bojčić, Katja. "Primjena logističke regresije pri procjeni rizičnosti korisnika kredita." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2019. https://urn.nsk.hr/urn:nbn:hr:148:598360

Bojčić, Katja. "Primjena logističke regresije pri procjeni rizičnosti korisnika kredita." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2019. https://urn.nsk.hr/urn:nbn:hr:148:598360

Bojčić, K. (2019). 'Primjena logističke regresije pri procjeni rizičnosti korisnika kredita', Master's thesis, University of Zagreb, Faculty of Economics and Business, accessed 26 June 2024, https://urn.nsk.hr/urn:nbn:hr:148:598360

Bojčić K. Primjena logističke regresije pri procjeni rizičnosti korisnika kredita [Master's thesis]. Zagreb: University of Zagreb, Faculty of Economics and Business; 2019 [cited 2024 June 26] Available at: https://urn.nsk.hr/urn:nbn:hr:148:598360

K. Bojčić, "Primjena logističke regresije pri procjeni rizičnosti korisnika kredita", Master's thesis, University of Zagreb, Faculty of Economics and Business, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:148:598360

Metadata
TitlePrimjena logističke regresije pri procjeni rizičnosti korisnika kredita
Title (english)The Use of Logistic Regression in Borrower Risk Assessment
AuthorKatja Bojčić
MentorSaša Jakšić (mentor)
Committee memberSaša Jakšić (predsjednik povjerenstva)
Committee memberLucija Rogić Dumančić (član povjerenstva)
Committee memberŽeljko Bogdan (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Economics and Business
(Department of Statistics)
Zagreb
Defense date and country2019-09-30, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Mathematics and Statistics
Abstract
Jedan od najčešćih i najprepoznatljivijih rizika u svakodnevnom poslovanju banaka jest kreditni rizik. Izloženost kreditnom riziku potiče banke na mudro i savjesno donošenje odluka s ciljem minimizacije rizika, što zahtjeva upravljanje kreditnim rizikom pomoću, između ostalog, kvantitativnih metoda. Time banka može predvidjeti rane znakove mogućeg nastupanja nepovoljnog događaja te prevenirati, odnosno amortizirati negativne posljedice. Razina kreditnog rizika klijenta često se određuje bankovnim internim kreditnim skoring modelima. Cilj takvih modela je donošenje sofisticiranih odluka kako bi se ustanovilo je li klijent uopće u mogućnosti uredno izvršavati, te u konačnici finalizirati otplatu dodijeljenih novčanih sredstava. Navedeno se najčešće provodi primjenom logističke regresije korištenjem povijesnih podataka i statističkih metoda. Cilj ovog rada je prikazati ulogu kreditnog vrednovanja u procesu donošenja odluka te u programskom jeziku R koristeći statistički skup podataka razvijene ekonomije demonstrirati na koji način model logističke regresije djeluje, uz predstavljanje same njegove uspješnosti usporedbom dobivenih modelom procijenjenih vrijednosti sa stvarnim vrijednostima
Abstract (english)
Credit risk is one of the most common and present risks in the day-to-day operations of banks. Exposure to credit risk encourages banks to make wise and conscientious decisions in order to minimize risk, which requires credit risk management using, among other things, quantitative methods. In this way, the bank can predict early signs of an adverse event and prevent or mitigate possible negative consequences. The client's credit risk level is often determined by the bank's internal credit scoring models. The goal of such models is to allow making sophisticated and rational decisions n oreder to determine clients’ credit worthiness. This is most often done by applying logistic regression using historical data and statistical methods. The purpose of this paper is to present the role of credit scoring in the decision-making process and, in R programming language using a real data, to demonstrate how the logistic regression model works and its performance accuracy by comparing the model's estimated values with the actual values.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:148:598360
Study programmeTitle: Economics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2019-10-07 17:37:14