The State of the Art of Academic Research on Non-performing Loans: A Structured Literature Review
Bibliographic record
Abstract
This study aims to conduct a Structured Literature Review (SLR) on Non-Performing Loans (NPLs), defined as distressed credits or deteriorated loans, to explore their historical developments and prospects. NPLs played a prominent role in the global financial landscape post the 2007 economic crisis and, nowadays, their volume is managed thanks to regulatory intervention. However, academic research on this topic is limited and sparse, particularly in relation to market volume and prices trend, as well as emerging management strategies and sustainability’s perspective. Therefore, our objective is to fill this gap and observe how the academic literature has responded to the development of this instrument. The SLR, and its associated bibliometric analysis, conducted using the Biblioshny package available on R-Studio, were performed on a sample of 1.236 academic documents (Articles, Book Chapters and Conference Papers) available on Scopus and published from 2010 to 2023. The sample, selected through a rigorous and validated screening procedure, was then studied across variables defined in the Analytical Framework: Topics, Research Methods, Research Area and Geographical Area. Based on this analysis, as explicitly stated in the findings section, we observe specific trending topics and geographical areas of study related to NPLs. This study not only helps fill a significant gap in academic literature concerning NPLs, but also provides important implications for financial practice and economic policy conducted by professionals, aiding in a better understanding of how to address and manage NPLs in various economic and geographical contexts. The originality of this research lies in its structured approach and use of bibliometric analysis, to examine a wide range of academic publications over an extended period, serving as a potential base for further insights and future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".