Journal of
Marketing Development and Competitiveness
Scholar Gateway
Abstracts prior to volume 5(1) have been archived!
Issue 5(1), October 2010 -- Paper AbstractsGirard (p. 9-22)
Cooper (p. 23-32)Kunz-Osborne (p. 33-41)Coulmas-Law (p.42-46)Stasio (p. 47-56)Albert-Valette-Florence (p.57-63)Zhang-Rauch (p. 64-70)Alam-Yasin (p. 71-78)Mattare-Monahan-Shah (p. 79-94)Nonis-Hudson-Hunt (p. 95-106) 
JOURNAL OF ACCOUNTING AND FINANCE
Artificial Intelligent Credit Risk Prediction: An Empirical Study of Analytical Artificial Intelligence Tools for Credit Risk Prediction in a Digital Era
Author(s): Diederick van Thiel, W. Fred van Raaij
Citation: Diederick van Thiel, W. Fred van Raaij, (2019) "Artificial Intelligent Credit Risk Prediction: An Empirical Study of Analytical Artificial Intelligence Tools for Credit Risk Prediction in a Digital Era", Journal of Accounting and Finance, Vol. 19, ss. 8, pp. 150-170
Article Type: Research paper
Publisher: North American Business Press
Abstract:
Millennials service expectations drive transformation from traditional lending into digital lending. The CAGR for digital lending is 53% until 2025. Therefore, in this growing information age new methods for credit risk scoring could form the central pillar for the continuity of a financial institution. This paper contains the first research into AI application in individual risk assessment across two advanced lending markets. The research has been performed on 133.152 mortgage and credit card customers of 3
European lenders during the period January 2016 – July 2017. As candidate models, we chose neural nets and random forests. The research describes three experiments that develop the artificial intelligent probability of default models. In all experiments AI models performed better than the traditional models. Scalable automated credit risk solutions can therefore build on AI in their risk scoring.
