Navigating Risks: How External Environments Shape Non-Performing Loans in Vietnam's Commercial Banks
Bibliographic record
Abstract
Objectives: The primary objective of this study is to analyze the factors influencing Non-Performing Loans (NPLs) in Vietnam's banking sector. It aims to shift the focus from traditional perspectives centered on macroeconomic indicators towards considering external environmental factors, such as global economic shifts, digital transformation, and industry-specific changes. Moreover, it seeks to examine the impact of the COVID-19 pandemic on NPL dynamics in Vietnamese commercial banks. Methods: The study employs empirical data collected from various Vietnamese commercial banks. Robust statistical methods are applied to analyze the data and explore the relationships between external environmental factors and NPLs. The research challenges conventional risk assessment models by advocating for a more comprehensive approach that integrates a broader spectrum of external influences beyond internal and macroeconomic variables. Results: The findings of the study suggest that external environmental factors, including global economic shifts, digital transformation, and industry-specific changes, significantly affect NPLs in Vietnam's banking sector. The analysis reveals the intricate dynamics of NPLs, especially under the influence of the COVID-19 pandemic. Contrary to traditional perspectives, the research underscores the importance of considering a wider range of factors in assessing and managing NPL risk. Conclusion: In conclusion, this study contributes significantly to the understanding of NPL dynamics in Vietnam's banking sector. The research highlights the need for banking executives and policymakers to adopt dynamic risk management strategies that account for external environmental factors. By recognizing the influence of global economic shifts, digital transformation, and industry-specific changes, stakeholders can enhance banking resilience and stability in emerging markets like Vietnam. The study underscores the importance of embracing a holistic approach to risk management in an era characterized by rapid global changes and uncertainties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".