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Submissions open

"Submissions Open For Vol. 11,Issue3, May. - June, 2024"


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Submissions open

"Submissions Open For Vol. 11,Issue3, May. - June, 2024"


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The use of the Improved XGBoost Algorithm in Credit Debt Risk Auditing

  • Xiangrong Shi
  • Qi Zheng
  • Pengsai Guo
  • Yifei Ye
In recent years, credit debt default events have occurred frequently, and the current credit debt default has become a common phenomenon in the capital market. Many high-rated companies have also defaulted on their bonds, and credit ratings are no longer sufficient to predict a company's default. This paper utilizes the mainstream Logistic algorithm and XGboost algorithm to construct a credit debt default risk method, the performance of the two is compared and analyzed, and the historical data information is used to improve the original algorithm, and the experimental results testify that the improved model prediction effect is better than the original algorithm, of which the enhanced-XGboost algorithm has the best comprehensive performance in various indicators, which is more suitable for credit debt risk audit.
Select Volume / Issue:
Year:
2022
Type of Publication:
Article
Keywords:
Credit Debt Default; Logistic; XGBoost; Algorithm Boosting; Risk Audits
Journal:
IJASM
Volume:
9
Number:
4
Pages:
61-72
Month:
July
ISSN:
2394-2894
Hits: 1500
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