Performance evaluation and risk analysis of online banking service
Bibliographic record
Abstract
Purpose Online banking has attracted a great deal of attention from various bank stakeholders such as bankers, financial service participants, and regulators. The purpose of this paper is to analyze the online banking service performance of giant US and UK banks. Risk analysis is also conducted. Design/methodology/approach This paper connects the principal component analysis (PCA) method with the data envelopment analysis (DEA) method to estimate the online banking performance. Data are collected from 2007 annual reports of giant banks in the USA and the UK including both financial and non‐financial variables. Findings Most giant banks are performing well based on DEA analysis. Employees turn out to be a key variable that contribute most to banks' revenue. Different DEA models can be classified into cost‐ and online‐oriented models, which is consistent with existing work based on data from other nations. Originality/value This paper presents a unique demonstration of using PCA and DEA for evaluation of giant banks with online banking service.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".