Izračun bihevijoralističkog kreditnog skoringa za klijente banke

master's thesis

• Author: Lucija Drašinac
• Mentor: Siniša Slijepčević (mentor)
• Committee member: Siniša Slijepčević (predsjednik povjerenstva)
• Committee member: Hrvoje Šikić (član povjerenstva)
• Committee member: Zoran Vondraček (član povjerenstva)
• Committee member: Sonja Štimac (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2018-11-27, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

Kreditni skoring numerički je sustav pomoću kojeg se ocjenjuje rizičnost klijenta kojemu se želi prodati neki proizvod. U ovome radu proučavamo statističke metode za izračun kreditnog skoringa. Na kraju opisujemo rezultate primjena modernih statističkih metoda i mašinskog učenja na uspješnu izradu vrlo prediktivnog behavijoralnog kreditnog skoringa.

Abstract (english)

Credit scoring is a numerical system used to assess the risk of a customer to whom a product is sold. In this thesis we consider statistical methods for calculating the credit scoring. Finally, we describe results of an application of modern statistical methods and machine learning to a successful development of very predictive behavioral credit scoring.

• Keywords: kreditni skoring; statističke metode izračuna; mašinsko učenje
• Keywords (english): credit scoring; statistical methods of calculation; machine learning
• Language: croatian
• URN:NBN: urn:nbn:hr:217:447733
• Study programme: Title: Mathematical Statistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-03-28 11:19:46