Recovery of Personal and Corporate Debts - Analysis of Selected Developed Countries in the World (preprint)
Bibliographic record
Abstract
The financial health of a developing country depends upon financial sectors as well as the allocation of financial resources. The wellbeing of a financial sector like Banks is a matter of policy concern for a developing country like India. The COVID-19 pandemic and lockdown has mounted Non-performing Assets problem for Governments around the world. Reserve Bank of India (RBI) in its financial stability report in July had indicated that due to COVID-19 Pandemic the asset quality of Indian Banks would worsen. The major focus of the paper is to analyse legal trends in various developed countries having effective debt recovery mechanism. The World Bank report 2019, on Doing Business ranked India at 108th of 190 countries on resolving insolvency which has improved from 16th in 2017. It is also noted by World Bank that insolvency procedure takes 4 to 3 years and costs about 9 percent of the sale proceeds of debtor’s estate. The meter of insolvency index has increased from 6 in 2017 to 8.5 in 2018. An Analysis of Insolvency regime in practice in various developed countries to promote economic stability and maximization of asset value, equitable distribution of proceeds, ensure transparency and predictability, recognition of existing creditor rights and establishment of clear rules for priority of ranking. The developed countries like Australia, Canada, U.K, and USA are having less than 2 per cent of NPA ratio (2015- 2019) whereas India’s NPA ratio is more than 8 per cent. These countries are having an effective debt recovery mechanism to tackle the Non-performing Assets. Indian economy falls under top economy category but it’s NPA ratio is more.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".