Credit Rating Model Based on Improved TabNet DOI Creative Commons
Shijie Wang, Xueyong Zhang

Mathematics, Journal Year: 2025, Volume and Issue: 13(9), P. 1473 - 1473

Published: April 30, 2025

Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional factors heterogeneous data fusion. This manuscript proposes an enhanced rating model based improved TabNet framework. First, Kaggle “Give Me Some Credit” dataset undergoes preprocessing, including balancing partitioning into training, testing, validation sets. Subsequently, architecture is refined through integration a multi-head attention mechanism to extract both global local feature representations. Bayesian optimization then employed accelerate hyperparameter selection automate parameter search for TabNet. To further enhance classification predictive performance, stacked ensemble learning approach implemented: serves as extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Boosting Machine), CatBoost (Categorical KNN (K-Nearest Neighbors), SVM (Support Vector Machine) are selected base learners first layer, with acting meta-learner second layer. The experimental results demonstrate that proposed TabNet-based outperforms benchmark models across multiple metrics, accuracy, precision, recall, F1-score, AUC (Area Curve), KS (Kolmogorov–Smirnov statistic).

Language: Английский

The impact of financial regulation on financial control efficiency: A comparative analysis of economies DOI Creative Commons
Іhor Rekunenko,

Artem Koldovskyi,

Kristina Babenko

et al.

Accounting and Financial Control, Journal Year: 2025, Volume and Issue: 6(1), P. 13 - 24

Published: March 3, 2025

A significant aspect of financial regulation provides for risk mitigation, transparency improvement, and maintaining economic stability, making control systems more efficient. This article analyzes the interaction strength with efficiency in five economies, such as USA, UK, Germany, Poland, China, from 2020 to 2023. An econometric model is utilized World Bank Financial Regulatory Index incorporated core independent variable, along infrastructure, modeling, GDP growth, inflation, leverage; all variables are used understand their effect on mechanisms. It confirmed that stronger UK Germany associated scoring by (the countries higher scores regulations better enforced have appropriate management strategies). On other hand, Poland China problems terms regulatory enforcement which translates into lower effectiveness control. The results also show inflation leverage decrease control, infrastructure modeling positively related efficiency. study emphasizes exigency regulating oversight emerging markets, strict policies, embracing technological advancements supplement area. future research agenda needs broaden scope economies qualitative assessments effectiveness.

Language: Английский

Citations

0
Strategic Significance of Machine Learning in Financial Services DOI

Shamrao Parashram Ghodake,

Jaya Saxena, Nitesh Behare

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 285 - 312

Published: April 18, 2025

Machine learning (ML) is transforming the financial services industry by driving innovation in fraud detection, risk management, customer personalization, and more. This chapter explores strategic significance of ML, its key applications, future trends shaping adoption. Integration with blockchain technology enhances security automation, while advancements quantum computing promise faster, more accurate models. However, challenges such as data privacy, algorithmic bias, regulatory compliance persist. The evolution frameworks growing importance explainable AI (XAI) are critical for ensuring transparency fairness. As institutions embrace these trends, they stand to enhance operational efficiency, decision-making accuracy, trust navigating complexities modern landscapes.

Language: Английский

Citations

0
Credit Rating Model Based on Improved TabNet DOI Creative Commons
Shijie Wang, Xueyong Zhang

Mathematics, Journal Year: 2025, Volume and Issue: 13(9), P. 1473 - 1473

Published: April 30, 2025

Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional factors heterogeneous data fusion. This manuscript proposes an enhanced rating model based improved TabNet framework. First, Kaggle “Give Me Some Credit” dataset undergoes preprocessing, including balancing partitioning into training, testing, validation sets. Subsequently, architecture is refined through integration a multi-head attention mechanism to extract both global local feature representations. Bayesian optimization then employed accelerate hyperparameter selection automate parameter search for TabNet. To further enhance classification predictive performance, stacked ensemble learning approach implemented: serves as extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Boosting Machine), CatBoost (Categorical KNN (K-Nearest Neighbors), SVM (Support Vector Machine) are selected base learners first layer, with acting meta-learner second layer. The experimental results demonstrate that proposed TabNet-based outperforms benchmark models across multiple metrics, accuracy, precision, recall, F1-score, AUC (Area Curve), KS (Kolmogorov–Smirnov statistic).

Language: Английский

Citations

0