Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Mesihovic, Aila Emma"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • No Thumbnail Available
    Item
    Artificial Intelligence and Machine Learning usage in credit risk management - A study from the Swedish financial services industry
    (2021-02-24) Mesihovic, Aila Emma; Nordström, Henrik; University of Gothenburg/Department of Business Administration; Göteborgs universitet/Företagsekonomiska institutionen
    Credit risk management is a fundamental process established in almost every financial institution. There are various tools and methods that financial institutions can use in order to mitigate risks among their loan takers. Credit scoring is a standard method used to evaluate risks among loan applicants, and it can be done by traditional statistical methods as well as Artificial Intelligence- and Machine Learning methods. This thesis presents a survey result among different Swedish financial institutions on their use of Artificial Intelligence and Machine Learning solutions in credit risk management. The results find that Artificial Intelligence and Machine Learning is moderately used with ambitions to further increase the use. The credit risk management process still heavily relies on traditional statistical credit scoring methods as there are regulations, end user perspectives, ethical dilemmas, and IT aspects that still need to be addressed in order to fully enable an implementation of Artificial Intelligence and Machine Learning in this area. A combination of traditional statistical and Artificial Intelligence and Machine Learning solutions is seen as an optimal way forward.

DSpace software copyright © 2002-2025 LYRASIS

  • Privacy policy
  • End User Agreement
  • Send Feedback