The role of artificial intelligence on limiting Jordanian commercial banks cybercrimes
Bibliographic record
Abstract
This study aims to investigate the role of applying artificial intelligence in limiting cybercrime in the 14 Jordanian commercial banks listed in the Amman Financial Market, from the point of view of internal auditors and IT. For this purpose, a questionnaire was designed and distributed to the study sample of (849). The number of respondents to the questionnaire reached (230). The data of the questionnaire were analyzed, and their hypotheses were tested using the statistical program (SPSS) through tests of the arithmetic mean, standard deviation, linear, multiple regression, and the T-test for two independent samples. The study found a statistically significant effect for artificial intelligence with its dimensions (Expert Systems, Artificial Neural Network, Genetic Algorithm, Fuzzy logic) in limiting cybercrime in Jordanian commercial banks. It was also found that there were statistically significant differences in both genetic algorithm and cybercrime attributable to the job variable in favor of IT department employees.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".