Exploring metaverse-enabled innovation in banking: Leveraging NFTS, blockchain, and smart contracts for transformative business opportunities
Bibliographic record
Abstract
Industries all throughout the world are preparing to understand the ramifications of the emerging metaverse, which is a merger of the virtual and physical worlds. Notably, the banking industry stands on the cusp of a monumental shift, with the metaverse offering unprecedented operational enhancements. While the potential transformations brought about by the metaverse are discussed in various sectors, there is a discernible gap in understanding its specific applications in banking, especially with respect to advanced technologies such as NFTs, blockchain, and smart contracts. The study adopts a comprehensive approach to bridge this knowledge gap, employing convenience non-probability sampling to engage 48 subject matter experts specializing in Metaverse-Enabled Innovation in Banking. Data was collected using both mailed and electronic questionnaires. The empirical analysis offers strong evidence supporting the pivotal role of technologies like Digital Twins, Artificial Intelligence, and Blockchain-Based Assets in the metaverse's preliminary stages. We discover a plethora of business potential for banks within the metaverse, including client communication, cross-border transactions, mortgages, digital assets, green loans, and data security.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".