• Login
    View Item 
    •   SU+ Home
    • Research and Publications
    • Strathmore Institute of Mathematical Sciences (SIMs)
    • SIMs Projects, Theses and Dissertations
    • BBSA Research Projects
    • BBSA Research Projects (2021)
    • View Item
    •   SU+ Home
    • Research and Publications
    • Strathmore Institute of Mathematical Sciences (SIMs)
    • SIMs Projects, Theses and Dissertations
    • BBSA Research Projects
    • BBSA Research Projects (2021)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Credit risk modelling in peer-to-peer lending: a comparative analysis of neural networks and XQboost

    Thumbnail
    View/Open
    Credit risk modelling in peer-to-peer lendinG a comparative analysis of neural networks and XQboost.pdf (10.24Mb)
    Date
    2021
    Author
    Wachira, Njomo
    Metadata
    Show full item record
    Abstract
    Consumer credit risk modelling involves coming up with the probability that a borrower default thus classifying borrowers as either defaulters or non-defaulters. This is important for lending firms because they are then able to lend out cash to borrowers who are most likely to repay on time. Tl1is protects their profits. This study aims to use machine learning techniques I in consumer credit risk modelling in a bid to find out which technique is more effective. In addition, the study aims at investigating the effect of new credit customers on default expenence.
    URI
    http://hdl.handle.net/11071/12551
    Collections
    • BBSA Research Projects (2021) [26]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of SU+Communities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV