ADAPTIVE ANOMALY CORRECTION AND DYNAMIC SAMPLE BALANCING FOR DEEP INTELLIGENT CREDIT RISK ASSESSMENT FRAMEWORK.

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Bibliographic Details
Title:	ADAPTIVE ANOMALY CORRECTION AND DYNAMIC SAMPLE BALANCING FOR DEEP INTELLIGENT CREDIT RISK ASSESSMENT FRAMEWORK.
Authors:	xueping, Zhu^1,2 zhuxueping@um.edu.cn, Samuri, Suzani Mohamad³ msuzani@meta.upsi.edu.my, Adnan, Muhamad Hariz Muhamad⁴ mhariz@meta.upsi.edu.my
Source:	NED University Journal of Research. 2026, Vol. 23 Issue 1, p44-69. 26p.
Subjects:	Autoencoders, Adaptive sampling (Statistics), Outliers (Statistics), Credit analysis, Classification, Financial technology, Deep learning
Abstract:	With the rapid development of fintech, credit risk assessment has become a critical task for financial institutions in managing loan default risks. This paper proposes an FGA-SAE model that integrates a Stacked Autoencoder (SAE) with a sample ratio adjustment algorithm, aiming to enhance classification accuracy and robustness in credit risk assessment. Experimental comparisons demonstrate that the complete FGA-SAE model exhibits superior performance in handling data imbalance and anomaly samples. Compared to models without sample ratio adjustment and anomaly sample correction, the complete FGA-SAE model achieves significantly improved classification accuracy andfaster convergence of loss values. The model effectively mitigates the impact of class imbalance on classification performance while significantly enhancing overall model performance through anomaly sample correction via the autoencoder. This study provides an innovative and efficient solution for credit risk assessment. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	With the rapid development of fintech, credit risk assessment has become a critical task for financial institutions in managing loan default risks. This paper proposes an FGA-SAE model that integrates a Stacked Autoencoder (SAE) with a sample ratio adjustment algorithm, aiming to enhance classification accuracy and robustness in credit risk assessment. Experimental comparisons demonstrate that the complete FGA-SAE model exhibits superior performance in handling data imbalance and anomaly samples. Compared to models without sample ratio adjustment and anomaly sample correction, the complete FGA-SAE model achieves significantly improved classification accuracy andfaster convergence of loss values. The model effectively mitigates the impact of class imbalance on classification performance while significantly enhancing overall model performance through anomaly sample correction via the autoencoder. This study provides an innovative and efficient solution for credit risk assessment. [ABSTRACT FROM AUTHOR]
ISSN:	2304716X
DOI:	10.35453/NEDJR-ASCN-2025-007.R4