Computer Crimes and Counter Measures in the Nigerian Banking Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increase in the use of the information and communication technology (ICT) facilities such as computers and the Internet in the perpetration of criminal activities like spamming, credit card frauds, ATM frauds, phishing, identity theft, denial-of-service, and a host of others has lend credence to the view that ICT is contributing to crime in the banking sector. A greater understanding of such computer crimes may complement existing security practices by possibly highlighting new areas of counter measures. This paper thus assesses whether these crimes can be totally eradicated or not and whether the new generation banks experience more computer crimes than the old generation banks in Nigeria. Based on the findings of this study, the paper concludes that total eradication of computer crimes is not possible but can be highly reduced if internal control measures are adequately put in place within a bank’s organizational structure and that new generation banks seem to experience more crimes than their old generation counterparts due to the fact that majority of their services, which are automated, are subjected to technological changes at a rapid rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 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 it