Outsourcing–design, process and performance, Michael J. Mol (2007), Cambridge, England: Cambridge University Press, 218 pp., ISBN: 978‐0‐521‐68278‐7
Bibliographic record
Abstract
FinTech is the term used to refer to financial and technology convergence space solutions.It usually refers to new innovations that conduct or connect with financial services via the internet, smart devices, software applications, or cloud services and encompasses anything from mobile banking to cryptocurrency applications.Despite the advantages of FinTech, cybercriminals seized the opportunity to exploit vulnerabilities in FinTech systems.Phishing attacks, ransomware, and data breaches have become more prevalent, targeting individuals and FinTech institutions.Bahrain, which is not different from the rest of the world, was impacted by such cyber threats.Thus, FinTech companies have had to strengthen their cybersecurity countermeasures and protocols to combat these threats.Existing countermeasures in the literature primarily focus on general cybersecurity practices and frameworks, with limited attention given to the specific needs of the FinTech industry.Hence, there is a notable gap in the literature regarding a focused cybersecurity framework that caters to the unique requirements of Fin-Tech innovations, especially in Bahrain.To bridge this gap, this research addresses the problem by conducting an extensive review of existing cybersecurity challenges, common practices, and cybersecurity standards and through in-depth research interviews with executives, experts, and other FinTech business stakeholders.Leveraging this knowledge, this research proposed an adaptable framework that addresses the risks and vulnerabilities faced by FinTech innovations in Bahrain.Through panel discussions and Delphi sessions, industry experts evaluated the framework's practical feasibility, ability to address specific risks, and compatibility with the existing FinTech regulatory landscape.The results demonstrate a high
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.036 |
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".