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scientific master's theses
Statistički valjano bodovanje za odobravanje kredita u bankama
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2010. urn:nbn:hr:168:814456
Librenjak, Helena
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Applied Computing

Cite this document

Librenjak, H. (2010). Statistički valjano bodovanje za odobravanje kredita u bankama (Scientific master's theses). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:814456

Librenjak, Helena. "Statistički valjano bodovanje za odobravanje kredita u bankama." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2010. https://urn.nsk.hr/urn:nbn:hr:168:814456

Librenjak, Helena. "Statistički valjano bodovanje za odobravanje kredita u bankama." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2010. https://urn.nsk.hr/urn:nbn:hr:168:814456

Librenjak, H. (2010). 'Statistički valjano bodovanje za odobravanje kredita u bankama', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 September 2024, https://urn.nsk.hr/urn:nbn:hr:168:814456

Librenjak H. Statistički valjano bodovanje za odobravanje kredita u bankama [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2010 [cited 2024 September 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:814456

H. Librenjak, "Statistički valjano bodovanje za odobravanje kredita u bankama", Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2010. Available at: https://urn.nsk.hr/urn:nbn:hr:168:814456

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Metadata
TitleStatistički valjano bodovanje za odobravanje kredita u bankama
Title (english)Statistically valid scoring for consumer credit applications in banking
AuthorHelena Librenjak
MentorDamir Kalpić (mentor)
Committee memberMirta Baranović (predsjednik povjerenstva)
Committee memberDamir Kalpić (član povjerenstva)
Committee memberKsenija Dumičić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Applied Computing)
Zagreb
Defense date and country2010-11-29, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
Svjetska tržišta bankovnih kredita imaju dugu tradiciju. Razvijena su, postoje i uspješno posluju godinama. U Hrvatskoj je tržište bankovnim kreditima u velikom usponu i još uvijek se razvija. Dubinska analiza podataka nalazi široku primjenu u razlicitim djelatnostima, pa tako i na financijskom tržištu. Jedan od temeljnih zadataka u podrucju bankarskog poslovanja je i razlucivanje dobrih klijenata od loših, odnosno predvidanje vjerojatnosti neplacanja duga vjerovnika. Postupci dubinske analize podataka omogucuju prepoznavanje skrivenih informacija u podacima koje posjeduju financijske institucije i otkrivanja novog znanja. Kreditno bodovanje oslanja se na prepoznavanje rizicne skupine koja je sklona neplacanju. Cilj je predvidjeti i smanjiti stopu neplacanja i delinkventnost klijenata, na osnovu znanja o postojecim klijentima koja se otkrivaju iz povijesnih informacija. U ovom radu iznesene su osnovne postavke i ideje na kojima se zasniva dubinska analiza podataka. Opisani su osnovni koraci u procesu otkrivanja znanja, te metode modeliranja statisticki valjane bodovne kartice. Za svaku pojedinu metodu definirane su njihove specificnosti, svojstva te njihove dobre i loše strane. Konacan cilj bio je primijeniti opisane metode u izradu bodovne kartice na odabranom uzorku i usporediti njihove rezultate. Provedena analiza i rezultati pokazuju da se sve navedene metode mogu koristiti u izradi statisticki valjane bodovne kartice, ali ne pokazuju jednaku sposobnost prepoznavanja dobrih od loših klijenata. Logisticka regresija pokazala je da je njena prediktivna snaga veca u odnosu na ostale metode i kao takva vec je naširoko prihvacena kao najbolja metoda za izradu bodovnih kartica za odobravanje kredita.
Abstract (english)
World market of bank loans has a long tradition; it is highly developed, and successfully operating for years. In Croatia, the market for bank loans is still in development and evolving. Data mining techniques are widely used in various industries, including financial market. One of the fundamental tasks in the field of banking industry is distinguishing good from bad clients, to be more precise predicting the probability of default of debt creditors. Data mining techniques allow recognition of hidden information in data that financial institutions posses and derivation of new knowledge. Credit scoring relies on identifying groups of customers with high risk that are inclined to credit default. The purpose of credit scoring is to predict and reduce default rate and client delinquency based on the knowledge of existing clients that is derived from historical data. This thesis summarises basic assumptions and ideas on which data mining is based. Basic steps in the process of knowledge discovery and statistically valid scorecard modelling methods are described and each method is defined by their characteristics, properties and their pros and cons. The ultimate aim was to apply the methods described in the scorecard development of the selected sample and compare their results. Conducted analysis and experimental results showed that all the methods can be applied when developing statistically valid scorecards, as they all have shown ability to distinguish good from bad clients, but with different scorecard power. The method that has shown to have the highest predictive power is the logistic regression that has already been wide accepted as the best method for application scorecard development.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:814456
ProjectNumber: 036-0361983-2022
Title: Održivi razvoj informacijskih sustava
Leader: Krešimir Fertalj
Jurisdiction: Croatia
Funder: MZOS
Funding stream: ZP
ProjectNumber: 036-0361983-3137
Title: Optimiranje i upravljanje rizicima u informacijskim sustavima
Leader: Damir Kalpić
Jurisdiction: Croatia
Funder: MZOS
Funding stream: ZP
Study programmeTitle: Postgraduate master programe in computing
Study programme type: university
Study level: postgraduate
Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Catalog URLhttp://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=34321
Type of resourceText
Extent97 str ; 30 cm
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2020-08-19 08:50:49

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