Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Deep Ordinal Reinforcement Learning Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Transfer Learning in Credit Risk

Authors : Hendra Suryanto, Charles Guan, Andrew Voumard, Ghassan Beydoun

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In the credit risk domain, lenders frequently face situations where there is no, or limited historical lending outcome data. This generally results in limited or unaffordable credit for some individuals and small businesses. Transfer learning can potentially reduce this limitation, by leveraging knowledge from related domains, with sufficient outcome data. We investigated the potential for applying transfer learning across various credit domains, for example, from the credit card lending and debt consolidation domain into the small business lending domain.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Deep Multi-task Approach for Residual Value Forecasting
next chapter Cold-Start Recommendation for On-Demand Cinemas
Footnotes
1
The hyper parameters optimization has been done before this step.
 
2
Associated with the outcome being predicted.
 
Literature
1.
Beydoun, G., Hoffmann, A.: Theoretical basis for hierarchical incremental knowledge acquisition. Int. J. Hum. Comput. Stud. 54(3), 407–452 (2001)CrossRef
2.
Breiman, L.: Some properties of splitting criteria. Mach. Learn. 24(1), 41–47 (1996)MATH
3.
Chattopadhyay, R., Ye, J., Panchanathan, S., Fan, W., Davidson, I.: Multi-source domain adaptation and its application to early detection of fatigue. In: KDD (2011)
4.
Flach, P.A.: The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 194–201 (2003)
5.
6.
Gao, J., Fan, W., Jiang, J., Han, J.: Knowledge transfer via multiple model local structure mapping. In: KDD (2008)
7.
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)CrossRef
8.
Pan, S.J., Yang, Q., et al.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRef
9.
Řezáč, M., Řezáč, F.: How to measure the quality of credit scoring models. Finance a úvěr Czech J. Econ. Finan. 61(5), 486–507 (2011)MATH
10.
Sun, S., Xu, Z., Yang, M.: Transfer learning with part-based ensembles. In: MCS (2013)
11.
12.
13.
Xiao, J., Wang, R., Teng, G.E., Hu, Y.: A transfer learning based classifier ensemble model for customer credit scoring. In: 2014 Seventh International Joint Conference on Computational Sciences and Optimization, pp. 64–68 (2014)
14.
Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMS. In: Proceedings of the 15th ACM International Conference on Multimedia, pp. 188–197. ACM (2007)
Metadata
Title
Transfer Learning in Credit Risk
Authors
Hendra Suryanto
Charles Guan
Andrew Voumard
Ghassan Beydoun
Copyright Year
2020
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-030-46132-4

Electronic ISBN: 978-3-030-46133-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-46133-1_29

Premium Partner

    Image Credits