Credit Scoring Prediction Using Deep Learning Models in the Financial Sector
The increasing complexity and volume of financial and behavioral data in modern credit scoring and risk assessment present significant challenges to traditional modeling methods. Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11087204/ |
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| Summary: | The increasing complexity and volume of financial and behavioral data in modern credit scoring and risk assessment present significant challenges to traditional modeling methods. Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals, limiting their ability to capture meaningful temporal and non-linear patterns. In the swiftly transforming domain of computational science, the incorporation of sophisticated machine learning algorithms has emerged as a critical driver in addressing these challenges. Although traditional statistical approaches provide foundational value, they frequently fall short when faced with the task of capturing the nuanced, non-linear associations embedded within extensive datasets, thereby limiting predictive precision. To address these shortcomings, we propose an innovative deep learning paradigm that effectively integrates structured financial information with unstructured behavioral data, thereby bolstering the reliability and accuracy of predictive analytics. Our approach features a thorough feature engineering pipeline that includes statistical indicators, temporal trends, and aggregated financial attributes—aimed at constructing a comprehensive representation of the data environment. At the core of our framework is a hybrid neural network architecture, which leverages Long Short-Term Memory (LSTM) units to handle sequential dependencies alongside dense layers that model complex interactions among features. This configuration enables the simultaneous learning of both temporal dynamics and high-level abstractions. To prevent overfitting and to promote better generalization, we incorporate adaptive regularization strategies that adjust penalization levels in response to validation metrics. We confront the issue of class imbalance by applying dynamic re-sampling and weight adjustment techniques, ensuring balanced model performance across varied data segments. Extensive evaluations on standard datasets validate the effectiveness of our proposed model, revealing enhanced prediction accuracy and model interpretability when compared to more traditional techniques. These results highlight the promise of advanced deep learning integration in computational science applications, offering a pathway toward more insightful and dependable predictive solutions. |
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| ISSN: | 2169-3536 |