Using Big Data to compare classification models for household credit rating in Kuwait

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dc.contributor.authorAlbarrak, Najla
dc.contributor.authorAlsanousi, Hessa
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorFilippone, Salvatore
dc.date.accessioned2021-09-10T14:11:58Z
dc.date.available2021-09-10T14:11:58Z
dc.date.issued2021-09-10
dc.description.abstractCredit rating risks have become the backbone of bank performance. They are the reflection of the current status of the bank and the milestone for future planning. A good credit assessment can better anticipate expected losses and will minimize unexpected losses from accumulating. Given advancements in technology as well as the big data available within banks about customers in an oil country such as Kuwait, a built-in model to help in-household credit scoring is at management’s decision. Compared with the current ‘black box’ rating models, we did a comparison between different classification models for two types of banking: conventional and Islamic. The classification models are as follows: Logistic Regression, Fine Decision Tree, Linear Support Vector Machines, Kernel Naïve Bayes, and RUSBoosted. Sufficiently, the last could be used to classify banks’ household customers and determine their default cases.en_UK
dc.identifier.citationAlbarrak N, Alsanousi H, Moulitsas I, Filippone S. (2021) Using Big Data to compare classification models for household credit rating in Kuwait. In: 6th International Congress on Information and Communication Technology, 25-26 February 2021, London, UKen_UK
dc.identifier.issn978-981-16-1780-5
dc.identifier.urihttps://doi.org/10.1007/978-981-16-1781-2_54
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17070
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCredit rating modelen_UK
dc.subjectCredit risken_UK
dc.subjectTechnologyen_UK
dc.subjectConventional bankingen_UK
dc.subjectIslamic bankingen_UK
dc.subjectClassification modelsen_UK
dc.subjectHousehold customersen_UK
dc.subjectMachine learningen_UK
dc.subjectLogistic regressionen_UK
dc.subjectFine decision treeen_UK
dc.subjectLinear support vector machinesen_UK
dc.subjectKernel Naïve Bayesen_UK
dc.subjectRUSBoosteden_UK
dc.titleUsing Big Data to compare classification models for household credit rating in Kuwaiten_UK
dc.typeConference paperen_UK

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