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September 5, 2025Emerging Science JournalOpen Access

Gradient Descent Decision Tree Algorithm and Nonlinear Programming for Credit Risk Assessment and Credit Strategy

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Authors

GCGuoqing ChenNCNipaporn ChutimanSSSujitta Suraphee

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Overview

This research develops a decision tree model to enhance credit risk assessment for SMEs, suggesting improved lending strategies.

Key Points

  • The proposed gradient descent decision tree algorithm achieved an AUC of 0.99, demonstrating superior accuracy.
  • With training set ratios of 70% and 80%, the model outperformed Adaptive Boosting (AUC = 0.97) and Random Forest (AUC = 0.91).
  • An ensemble learning approach was integrated with regularization in the decision tree model for enhanced prediction accuracy.
  • The study offers a new solution for credit assessment, assisting banks in optimizing lending strategies amidst risk constraints.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c23965b210217d6477b79fhttps://doi.org/10.28991/esj-2025-09-04-05
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI-Driven Credit Risk Models for Small-Scale Lending: A Business Analytics Framework for Predictive Performance and Responsible Deployment2025 · 1 citations
  2. 2Machine Learning-Based Credit Risk Prediction: A Systematic Review of Techniques, Challenges, and Future Directions2025 · 1 citations
  3. 3Application of a Machine Learning Algorithm to Assess and Minimize Credit Risks2025
  4. 4Application of Machine Learning Algorithm to Assess and Minimize Credit Risks2025
  5. 5Comparative Analysis Research on Machine Learning Models in Credit Risk Assessment2025