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Analiza tveganj pri spletnih posojilih : delo diplomskega seminarja
Zukanovič, Jure (Author), Orbanić, Alen (Mentor) More about this mentor... This link opens in a new window

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Abstract
Rdeča nit tega dela diplomskega seminarja je predstavitev in primerjava različnih metod strojnega učenja za identificiranje "dobrega izposojevalca", na podlagi podatkov priljubljene spletne platforme za posojanje denarja Lending Club (kratica LC). V delu so predstavljeni rezultati ugotovitev članka z naslovom Risk assessment in social lending via random forests, avtorjev Milada Malekipirbazarija in Vurala Aksakallija, ki sta z uporabo programa WEKA prikazala, da je matoda naključnih gozdov pri identificiranju "dobrega izposojevalca" boljša od točk FICO in ocene s strani LC. To sta metodi, ki ju za identificiranje "dobrega izposojevalca" dandanes uporabljajo agencije, ki se ukvarjajo z računanjem kreditne ocene posameznika za spletne posojilnice. V zaključku dela je predstavljena rekonstrukcija in potrditev navedb članka na podlagi rezultatov, ki sem jih dobil, ko sem simulacije v programu WEKA zagnal še sam. Metode strojnega učenja, ki so predstavljene v tem delu diplomskega seminarja, so: metoda naključnih gozdov, logistična regresija, metoda podpornih vektorjev in metoda k-najbližjih sosedov.

Language:Slovenian
Keywords:finančna matematika, posojanje denarja prek spleta, ocena tveganja, spletna posojilnica, posojevalec, izposojevalec, strojno učenje, naključni gozd, logistična regresija, metoda podpornih vektorjev, metoda k-najbližjih sosedov
Work type:Final seminar paper (mb14)
Tipology:2.11 - Undergraduate Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2017
Publisher:[J. Zukanovič]
Number of pages:28 str.
UDC:512
COBISS.SI-ID:18553945 Link is opened in a new window
Views:412
Downloads:162
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Secondary language

Language:English
Abstract:
The cohesive thread of the following seminar thesis is the presentation and comparison of different methods of machine learning to identify a "good borrower", according to the popular online lending platform Lending Club (acronym LC). The paper presents the results of the findings of an article entitled Risk assessment in social lending via random forests by Milad Malekipirbazari and Vural Aksakalli, who, with the help of WEKA program, showed that the random forests method in identifying a "good borrower" is better than FICO score and LC grades. These methods for identifying a "good borrower" are used by today's agencies to identify the credit rating of an individual for social lending. In the conclusion of the thesis's seminar, a reconstruction and confirmation of results of the article is presented, based on results I have obtained after running the simulations in WEKA program first-hand. Machine learning methods presented in this thesis's seminar are: random forests, logistic regression, support vector machines, and k-nearest neighbours method.

Keywords:mathematics, peer-to-peer lending, P2P lending, risk assessment, social lending platform, lender, borrower, machine learning, random forests, logistic regression, support vector machines, k-nearest neighbors

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