Communication in Banking Sector: A Systematic Review
Bibliographic record
Abstract
Background: Effective banking communication strengthens the relationship between customer, suppliers, stakeholders, manager, client, employees and board of directors. Banker’s experience on banking communication enhances banking system, employee’s behavior and core banking services facilities management. Banking communication improves organizational effectiveness through training, knowledge management, risk management, internal control system and data security management. In modern era, communication channel is transformed into electronic channel promoting e-banking which includes internet banking, mobile banking and e-payment system. Moreover, clerical work shifted into electronic form, which cut costs and satisfy customer. Objectives: This paper explores how banker’s experience effects on banking communication in commercial banks, which enhances their understanding level and determines effective communication in workplace. Methods: Extensive desk reviews followed by related literature werecarried out to gain better insight regarding the field of study. Findings: With the passage of time, computers and technologies have changed the traditional method of communication system. Banks are now using windows, world processing system, excel, computer operating system, DOS, database management system, data planning and database design, data security, internet, intranet, extranet service and email system which increase working performance. Conclusions: Bank managers need to understand importance of communication skills in order to increase effectiveness of internal communication between manager and employees. Implications: Commercial banks of Nepal requireto update in their communication practices and strategies in order to build proper channel which would help in communication between employees and management.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".