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<p>Against the backdrop of dynamic transformations in the financial sector and prominent corporate diversification trends, credit risk prediction becomes significantly more challenging. On one hand, this study focuses on optimizing the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm for corporate credit risk prediction, thereby enhancing financial institutions’ risk management capabilities. The study systematically examines corporate diversification strategies, revealing discrepancies between theoretical frameworks and practical implementations. These strategies complicate corporate financial structures, generating divergent profitability, capital allocation, and risk profiles across business units. Such heterogeneity leads to uneven resource distribution, ultimately impacting enterprise operations, debt servicing capacity, and credit performance. Consequently, financial institutions increasingly prioritize cross-sectoral risk monitoring, business synergy evaluation, and dynamic financial tracking. On the other hand, regarding algorithmic innovation, this study conducts an in-depth analysis of SMOTE’s fundamental principles, encompassing its sample generation mechanics and optimized variants. Especially in terms of optimization content, this study innovatively introduces an adaptive boundary adjustment mechanism that automatically defines minority class boundaries based on data distribution characteristics. Meanwhile, it precisely targets critical oversampling areas while avoiding arbitrary sample generation in irrelevant regions. Moreover, an optimized weight allocation protocol during synthetic sample creation incorporates feature relevance and class distribution to produce more representative new samples. The experimental framework utilizes four benchmark datasets: German Credit, Australian Credit Approval, Taiwan Credit Card Default, and Corporate Credit Risk Assessment. The rigorous methodology ensures research validity through scientific data partitioning, appropriate hardware configuration, an advanced software environment, and comprehensive evaluation indices (accuracy, precision, recall, F1-score). The study mainly focuses on two aspects. One is to analyze corporate diversification strategy; the other is to explore the optimization of the SMOTE algorithm and its application in credit risk prediction. Empirical results demonstrate the optimized SMOTE algorithm’s superiority over six comparison models, such as random over-sampling, under-sampling, etc. The accuracy rate is improved by more than 21%, and the highest is close to 38%. Precision is enhanced by over 28%, peak nearly 35%; Recall is increased by more than 31% and peaked at 42%; F1 score is boosted by approximately 33%, with a maximum of about 39%. This study provides financial institutions with an advanced algorithmic solution for credit risk assessment in diversified corporate environments. Also, it is expected to improve decision-making accuracy, strengthen risk resilience, and promote financial market stability.</p>

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SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration

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  • 发表日期：

    2025年7月2日

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  • Han Wei

摘要

Against the backdrop of dynamic transformations in the financial sector and prominent corporate diversification trends, credit risk prediction becomes significantly more challenging. On one hand, this study focuses on optimizing the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm for corporate credit risk prediction, thereby enhancing financial institutions’ risk management capabilities. The study systematically examines corporate diversification strategies, revealing discrepancies between theoretical frameworks and practical implementations. These strategies complicate corporate financial structures, generating divergent profitability, capital allocation, and risk profiles across business units. Such heterogeneity leads to uneven resource distribution, ultimately impacting enterprise operations, debt servicing capacity, and credit performance. Consequently, financial institutions increasingly prioritize cross-sectoral risk monitoring, business synergy evaluation, and dynamic financial tracking. On the other hand, regarding algorithmic innovation, this study conducts an in-depth analysis of SMOTE’s fundamental principles, encompassing its sample generation mechanics and optimized variants. Especially in terms of optimization content, this study innovatively introduces an adaptive boundary adjustment mechanism that automatically defines minority class boundaries based on data distribution characteristics. Meanwhile, it precisely targets critical oversampling areas while avoiding arbitrary sample generation in irrelevant regions. Moreover, an optimized weight allocation protocol during synthetic sample creation incorporates feature relevance and class distribution to produce more representative new samples. The experimental framework utilizes four benchmark datasets: German Credit, Australian Credit Approval, Taiwan Credit Card Default, and Corporate Credit Risk Assessment. The rigorous methodology ensures research validity through scientific data partitioning, appropriate hardware configuration, an advanced software environment, and comprehensive evaluation indices (accuracy, precision, recall, F1-score). The study mainly focuses on two aspects. One is to analyze corporate diversification strategy; the other is to explore the optimization of the SMOTE algorithm and its application in credit risk prediction. Empirical results demonstrate the optimized SMOTE algorithm’s superiority over six comparison models, such as random over-sampling, under-sampling, etc. The accuracy rate is improved by more than 21%, and the highest is close to 38%. Precision is enhanced by over 28%, peak nearly 35%; Recall is increased by more than 31% and peaked at 42%; F1 score is boosted by approximately 33%, with a maximum of about 39%. This study provides financial institutions with an advanced algorithmic solution for credit risk assessment in diversified corporate environments. Also, it is expected to improve decision-making accuracy, strengthen risk resilience, and promote financial market stability.