arbiter
JournalsPricingAccount

Credit risk detection based on machine learning algorithms

Xin Wang et al.

International Journal of Financial Services Management2022https://doi.org/10.1504/ijfsm.2022.126871article
ABDC C
Weight
0.39

What the paper says

As the global economic environment has become more complicated in recent years, more and more credit bonds have defaulted. The credit risk early warning model plays a very effective role in preventing and controlling financial risk and debt default. This paper uses machine learning methods to establish a credit default risk prediction framework. In this paper, the oversampling technique is first applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared. The empirical results show that the performance of the ensemble learning algorithms is the best.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijfsm.2022.126871

Or copy a formatted citation

@article{xin2022,
  title        = {{Credit risk detection based on machine learning algorithms}},
  author       = {Xin Wang et al.},
  journal      = {International Journal of Financial Services Management},
  year         = {2022},
  doi          = {https://doi.org/https://doi.org/10.1504/ijfsm.2022.126871},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Credit risk detection based on machine learning algorithms

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.39

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.10 × 0.4 = 0.04
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.