Factors Affecting Non-Performing Loans of Commercial Banks in Malaysia
Bibliographic record
Abstract
With the economy’s growth, the banking industry expands, and the competitiveness intensifies with the increased number of banks. Nevertheless, its non-payment also leads to huge losses for banks and the country. One of the critical determinants of the banking sector's performance is the loans advanced to get the profit. Therefore, the loans and their repayments are used as the comparison. Specifically, banks note their repayments seriously, and those default loans are declared non-performing loans (NPLs). Thus, NPL indicates a country’s banking system’s health. This study investigates the factors influencing Non-Performing Loans (NPLs) in the commercial banking sector of Malaysia from 2012 to 2021. NPLs are considered a crucial indicator of the banking system's health and the overall economic health of a country. The study examines the relationship between bank-specific and macroeconomic factors and their impact on NPLs. The bank-specific factors analyzed are loan-to-deposit ratios, capital adequacy ratios, and bank size. In contrast, macroeconomic factors are unemployment, inflation, and gross domestic product. Data were collected from published annual reports, the World Bank website, and DataStream navigators for ten years from 2012 to 2021, involving 26 commercial banks in Malaysia. Data analysis includes Descriptive Analysis, Correlation, Multicollinearity, and Multiple Regression Analysis using SPSS version 20 software. The result indicates that loan-to-deposit ratio, bank size, unemployment rate, and gross domestic product significantly impact the NPLs in the Malaysian Commercial Banks industry. Meanwhile, the capital adequacy ratio and inflation rate did not affect the NPLs in Malaysia.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".