Click of loan moratorium in Malaysia / Fauziah Mohamad Yunus and Anita Abu Hassan
Bibliographic record
Abstract
The COVID-19 pandemic has brought over severe health and economic crisis. According to the World Health Organisation (WHO), this illness will affect the lower respiratory tract that include windpipe and lungs, as well as the upper respiratory tract involving sinuses, nose, and throat. Fever, coughing, and shortness of breath are a few of the warning signs and symptoms of COVID-19. Infection can lead to pneumonia, severe acute respiratory syndrome, and, in the worst case, death. Lockdowns have been implemented in numerous nations, including Malaysia as the best alternative to stop the spread of this deathly virus. Both the financial condition of the businesses and the individual bank borrowers had been affected by this lockdown measure. Many Malaysians are losing their jobs, going on unpaid leave, or having their salaries deducted, and many small and medium enterprises are going out of business. In easing the financial burden of the Malaysian, starting in April 2020, Bank Negara Malaysia has announced a six-month automatic moratorium on all bank loans to help borrowers who suffer temporary financial difficulties. Malaysia, according to Finance Minister Tengku Datuk Seri Zafrul Tengku Abdul Aziz, is the only government that has automatically extended a loan moratorium to all debtors. Individual and business borrowers can benefit from the deferment package to postpone loan payments during the moratorium period. Except for credit card bills, the postponement includes conventional and Islamic financing repayment obligations such as housing loans and hire purchase loans. As such, no legal action will be taken against the borrowers and they are exempted from making monthly payments on hire purchase and home loans throughout the six months period beginning in April 2020. According to Mehta and Kaul (2020), moratoriums are not a new notion in the Indian banking sector, having been granted on multiple occasions previously. Debt or foreclosure moratoriums have also been implemented in nations such as the United State of America, Canada, Greece, Italy, the United Kingdom, Singapore, Thailand, Indonesia, and the Philippines (Bedi & Tan, 2020). Meanwhile, after the 2008 housing crisis, Japan offered a moratorium for SMEs, but the repercussions of the protracted moratorium period were mostly unfavourable, and the scheme giving the moratorium was widely regarded as a failure. The idea of a moratorium continues to be unclear to many individuals. The loan moratorium just delays payments at which it does not waive the borrower's obligation to pay interest to the bank. The benefits of a moratorium are obtained when it allows borrowers to plan their repayment strategy without stress, and the moratorium period does not affect an individual’s ability to borrow, as it has no negative effects on the borrower’s credit score. Unfortunately, the moratorium will give some difficulties to the borrowers, as they will be charged accumulated interest on deferred payments, which includes any outstanding principal and interest. The implementation of loan moratorium automatically leads to a longer loan tenures. To be clear, this is only a delayed payment instalment by customers to banks without any charges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".