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dc.contributor.advisorKhriyenko, Oleksiy
dc.contributor.advisorKarimova, Rahima
dc.contributor.advisorFredström, Ashkan
dc.contributor.authorHossain, Mohammad Farhad
dc.date.accessioned2021-04-16T05:25:03Z
dc.date.available2021-04-16T05:25:03Z
dc.date.issued2021
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/75073
dc.description.abstractPredicting ‘default’ behavior of borrowers is quite challenging and time consuming, although financial institutions require faster and more reliable decision on loan applications to survive in the competitive market. Availability of huge amount of data makes the work of current credit scoring system harder. To deal with such situation machine learning engineers are trying to build a system that can predict default behavior of a borrower by analyzing application and transaction data. In our current study we applied different machine learning models such as decision tree, logistic regression, gradient boosting, XGBoosting, support vector machine and KNeighbors on transactional dataset to find which model performed better. We also applied deep neural network on the datasets. To further extend the study, we created new features by using manual process and unsupervised machine learning to observe whether they boost the performance or not. In addition to that, we used feature selection to see how it affected the prediction. Due to small dataset, we achieved 70% ac-curacy with 72% AUC on aggregated dataset from Random Forest. The dataset created by using unsupervised machine learning showed 62% accuracy with 68% AUC value. Manually created ratio-based features and feature selection could not yield any significant difference in results. Deep learning also per-formed lower than others probably due to small dataset.en
dc.format.extent56
dc.language.isoen
dc.subject.otherdeep learning
dc.subject.othercredit scoring
dc.subject.othertransaction data
dc.subject.otherdefault behavior
dc.subject.otherloan application
dc.titleStudy of various machine learning approaches to predict default behavior of a borrower based on transactional dataset
dc.identifier.urnURN:NBN:fi:jyu-202104162383
dc.type.ontasotMaster’s thesisen
dc.type.ontasotPro gradu -tutkielmafi
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformaatioteknologiafi
dc.contributor.laitosInformation Technologyen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.rights.copyrightJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rights.copyrightThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.contributor.oppiainekoodi602
dc.subject.ysokoneoppiminen
dc.subject.ysorahoituslaitokset
dc.subject.ysomachine learning
dc.subject.ysofinancial institutions


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