Revolutionizing Banking: Neobanks’ Digital Transformation for Enhanced Efficiency
Bibliographic record
Abstract
Changes in customer behaviors after the COVID-19 pandemic have encouraged the transformation of banking systems. Neobanks have emerged as an innovation and entered the banking system to compete with traditional banks by offering new customer experiences. Neobanks transform traditional banking products and services which are delivered through physical interactions into those delivered via digital channels. This paper analyzes traditional banks that have transformed into neobanks, specifically their efficiency after digital transformation. Efficiency was measured using Stochastic Frontier Analysis (SFA), as it is highly accurate in estimating efficiency scores. This study also used a Pooled Mean Group (PMG) estimation of the Panel ARDL (Autoregressive Distributed Lag), as this approach is useful for analyzing the relationship between variables in panel data, to investigate digital transformation as a determinant of neobanks’ efficiency and examine the existence of short-term and long-term relationships between digital transformation and efficiency. We found that the efficiency of neobanks increases after digital transformation. Furthermore, it can be concluded that digital transformation is a determinant of efficiency and that there is long-term relationship between digital transformation and efficiency. In the short term, digital transformation has a significant negative correlation with efficiency, but in the long term, it has a significant positive relationship; this is because the cost of digital transformation initially decreases the profit efficiency, but afterwards, it increases the efficiency.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".