Technological Transition of Banks for Development: New Information and Communication Technology and Its Impact on the Banking Sector in Lebanon
Bibliographic record
Abstract
The banking industry has been facing many challenges during the last decade. One of these challenges is the technology transmission in its overall processes, not only in the implementation and processing of financial innovation operations like securitization newly learnt today in the banking industry in Lebanon, but also in the transition towards innovation in the internal way of working of these banks. New information and communication technology has been integrating the indirect financial system to fasten, empower and ease access to all users. It is a tool which impact has not been yet addressed by researchers and that is interesting to investigate in this survey. Consequently, two core questions narrow the research question. The first is: what are the opportunities and motivations of Lebanese commercial banks behind implementation of NICTs? The second question is: what are the effects of NICT on the production function, on the distribution function and on the productivity function of these banks? After defining in the conceptual part the banking businesses and their evolution, we will study the impact of technological change on the functioning of the bank. The second part is devoted to empirical validation through which two qualitative questionnaires are analyzed. The main purpose of this paper is to analyze first the changes and mutations of the various professions of the bank through technological transition and second to measure technological performance of Lebanese Commercial Banks. Our investigations confirm that new technologies play an important role in the development of the banking industry, strengthening their profitability and improving their productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".