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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".