Transfer Learning in Credit Risk
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.
Cite
Text
Suryanto et al. "Transfer Learning in Credit Risk." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_29Markdown
[Suryanto et al. "Transfer Learning in Credit Risk." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/suryanto2019ecmlpkdd-transfer/) doi:10.1007/978-3-030-46133-1_29BibTeX
@inproceedings{suryanto2019ecmlpkdd-transfer,
title = {{Transfer Learning in Credit Risk}},
author = {Suryanto, Hendra and Guan, Charles and Voumard, Andrew and Beydoun, Ghassan},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {483-498},
doi = {10.1007/978-3-030-46133-1_29},
url = {https://mlanthology.org/ecmlpkdd/2019/suryanto2019ecmlpkdd-transfer/}
}