• Deep Learning Approaches in Credit Scoring 

      Hjelkrem, Lars Ole (Doctoral theses at NTNU;2023:256, Doctoral thesis, 2023)
      SUMMARY Credit scoring is a crucial aspect of the lending process, as it helps lenders to assess the creditworthiness of potential borrowers. The main focus of this thesis is applying deep learning methods on transaction ...
    • Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data 

      Hjelkrem, Lars Ole; de Lange, Petter Eilif (Peer reviewed; Journal article, 2023)
      Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning ...
    • Stability and accuracy of credit ratings Examining credit assessments from two Norwegian banks 

      Hua, Eric Guangcheng; Jacobsen, Jesper T; De Lange, Petter Eilif; Hjelkrem, Lars Ole (Peer reviewed; Journal article, 2021)
      Does the novel technology blockchain conceal properties of an organization that we do not see? This paper suggests that this may be the case. The paper sets out to substantiate a claim that we might be observing the emergence ...
    • The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank 

      Hjelkrem, Lars Ole; De Lange, Petter Eilif; Nesset, Erik (Peer reviewed; Journal article, 2022)
      Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, ...