Economic Effects of the Digital Transformation on the Banking Market Using the Example of Savings Banks and Cooperative Banks in Germany
Bibliographic record
Abstract
Due to the digital transformation, the banking sector in Germany is undergoing massive change. This structural change is massively influenced by technological progress, regulation and supervision, the low-interest phase and demographic change. The focus of this research is on the comparison of savings banks and cooperative banks in Germany, as there are many similarities between the two banking groups. Both belong to the so-called retail banks. The respective bank clients are very similar due to the regional principle, the structure in regional associations and in their clientele. The main purpose of this research is to investigate which of the two banking groups, savings banks or cooperative banks, is more operationally efficient under the same prevailing competitive pressure from the Digital Transformation. This paper summarises the analysis of both banking groups based on real ratios. The relevance of the findings on this scientific problem is that the comparison of savings banks and cooperative banks in Germany has not been addressed in the scientific literature so far. The aim of the research is to make a statement as to which banking group has performed better given the same external market factors. Furthermore, arguments and counter-arguments within the academic discussion on the topic of digitalization in the German banking market will be compiled. The results of the research can be useful for academics who deal with the digital transformation in the banking sector in Germany.
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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 | 0.000 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".