Cyber Risk Analytics and Security Frameworks for Safeguarding U.S. Digital Banking Infrastructure
Bibliographic record
Abstract
This study explores the current landscape of information security policies and practices within the U.S. banking sector, while offering comparative insights from global banking systems. Using a qualitative approach and guided by PRISMA 2020 standards, the research involved a systematic review of 125 academic papers and 20 reports sourced from databases such as Scopus, Web of Science, and Google Scholar. The analysis reveals that U.S. banks face a wide array of cybersecurity threats, including phishing, ransomware, insider risks, and regulatory challenges. Nevertheless, robust security frameworks anchored by legislation like the Gramm-Leach-Bliley Act and supported by partnerships with agencies such as CISA and FS-ISAC—have helped mitigate these risks by fostering trust, enhancing fraud detection through AI, and maintaining financial stability. Global comparisons highlight shared concerns over evolving threats, regulatory compliance, and the importance of international collaboration. While the U.S. demonstrates strong regulatory foundations, areas for improvement include enhanced employee training, broader adoption of advanced cybersecurity tools, and greater cross-border coordination. The study concludes by recommending multi-factor authentication, AI and blockchain integration, and increased employee awareness initiatives. However, it notes limitations such as the reliance on secondary data and a U.S.-centric focus. Future research should include primary data and quantitative evaluations to better understand the effectiveness of cybersecurity investments. These findings offer practical guidance for policymakers and banking stakeholders aiming to strengthen cyber resilience in an increasingly digital financial environment.
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.
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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".