Key Trends Driving Adoption of Generative Artificial Intelligence in Malaysian Banking Sectors
Bibliographic record
Abstract
The financial sectors in Malaysia are being influenced by worldwide occurrences, like regulatory changes, financial reforms, technological disruptions and advancements, demographics, sociopsychological factors, healthcare developments, global trade dynamics, and geopolitical events. Consequently, banks are making substantial investments and ramping up funding, particularly in the field of Artificial Intelligence (AI), to mitigate the risk of disruptions. With the pandemic experiences, AI has been broken down for better management into Machine Learning (ML), Deep Learning (DL) and Generative AI. Tools and companies are proliferating at an astonishing rate evolving in the Banking ecosystem since 2021. In this research, the key trends currently driving adoption of Generative AI in the Malaysian Banking Sectors are investigated using a convenience survey method from 5 banks in Malaysia. The results show a variety of workforce transformations that will be critical to creating an agile and fit-for-future financial personnel.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".