03594ntm a22005057a 4500 000262699 CZ-PrVSE 20140920002718.0 m d cr n|||||||||| 140920s2014 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 Haltuf, Michal ISIS:49331 dis Podpůrné vektorové stroje pro kreditní skóring eng Support vector machines for credit scoring [elektronický zdroj] / Michal Haltuf 2014 ?? s. : digital, PDF soubor Vedoucí práce: Jiří Witzany Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2014 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2014 Quantitative methods to assess the creditworthiness of the loan applicants are vital for the profitability and the transparency of the lending business. With the total loan volumes typical for traditional financial institutions, even the slightest improvement in credit scoring models can translate into substantial additional profit. Yet for the regulatory reasons and due to the potential model risk, banks tend to be reluctant to replace the logistic regression as an industrial standard with the new algorithms. This does not stop researchers from examining such new approaches, though. This thesis discusses the potential of the support vector machines, to become an alternative to logistic regression in credit scoring. Using the real-life credit data set obtained from the P2P lending platform Bondora, the scoring models were built to compare the discrimination power of support vector machines against the traditional approach. The results of the comparison were ambiguous. The linear support vector machines performed worse than logistic regression and their training consumed much more time. On the other hand, support vector machines with non-linear kernel performed better than logistic regression and the difference was statistically significant at 95% level. Despite this success, several factors prevent SVM from the widespread applications in credit scoring, higher training times and lower robustness of the method being two of the major drawbacks. Considering the alternative algorithms which became available in the last 10 years, support vector machines cannot be recommended as a standalone method for credit risk models. Způsob přístupu: Internet finanční inženýrství [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas peer to peer lending credit scoring logistic regression support vector machines Witzany, Jiří, 1966- vse2007429830 ths Kolman, Marek vse2015859792 opn Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví kn20010709398 https://isis.vse.cz/zp/46349/podrobnosti VŠKP v InSIS https://isis.vse.cz/zp/46349 Hlavní práce https://isis.vse.cz/zp/46349/posudek/vedouci Hodnocení vedoucího https://isis.vse.cz/zp/46349/posudek/oponent/39428 Oponentura http://isis.vse.cz/zp/138962/podrobnosti dc:identifier NEPOSILAT VSKP 138962 vse46349 140919