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master's thesis
Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:076269
Puljak, Toma
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Puljak, T. (2023). Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:076269

Puljak, Toma. "Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:076269

Puljak, Toma. "Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:076269

Puljak, T. (2023). 'Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 29 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:076269

Puljak T. Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 29] Available at: https://urn.nsk.hr/urn:nbn:hr:168:076269

T. Puljak, "Objašnjivi modeli strojnog učenja za procjenu kreditnog rizika", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:076269

Metadata
TitleObjašnjivi modeli strojnog učenja za procjenu kreditnog rizika
Title (english)Explainable Machine Learning Models for Credit Risk Assessment
AuthorToma Puljak
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
Committee memberStjepan Begušić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-13, Croatia
Abstract
Zbog regulacija koje su postavljene od Europskog bankovnog tijela, svi modeli strojnog učenja koji se koriste u procjeni kreditnog rizika moraju biti u potpunosti objašnjivi. Zbog tih regulacija, financijske institucije pri procjeni kreditnog rizika koriste jednostavnije modele strojnog učenja, poput logističke regresije, koji često nemaju najveću točnost. Ovaj diplomski rad istražuje različite slojeve objašnjivosti koje treba zadovoljiti model "crne kutije", specifično XGBoost, kako bi bio u skladu s trenutnom regulativom i predlaže izračun doprinosa značajki promjeni PD-a i migraciji kategorije rizika. Obje su implementirane metode zadovoljile trenutne propise i financijske institucije bi ih mogle koristiti za implementaciju XGBoosta za procjenu kreditnog rizika.
Abstract (english)
Because of regulations posed by the European Banking Authority, all machine learning models used in credit risk assessment need to be fully explainable. This made financial institutions resort to using simple machine learning models, such as logistic regression, when assessing credit risk, which often do not have highest accuracies. This thesis explores different explainability layers that need to be satisfied by a "black-box" model, specifically XGBoost, to comply with current regulation and proposes the calculation feature contributions to PD change and risk category migrations. Both methods proved to satisfy current regulations and could be used by financial institutions to deploy XGBoost for credit risk assessment.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:076269
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-10-11 10:15:33