A Review on Risk Management in Information Systems: Risk Policy, Control and Fraud Detection
Bibliographic record
Abstract
Businesses are bombarded with great deals of risks, vulnerabilities, and unforeseen business interruptions in their lifetime, which negatively affect their productivity and sustainability within the market. Such risks require a risk management system to identify risks and risk factors and propose approaches to eliminate or reduce them. Risk management involves highly structured practices that should be implemented within an organization, including organizational planning documents. Continuity planning and fraud detection policy development are among the many critically important practices conducted through risk management that aim to mitigate risk factors, their vulnerability, and their impact. Information systems play a pivotal role in any organization by providing many benefits, such as reducing human errors and associated risks owing to the employment of sophisticated algorithms. Both the development and establishment of an information system within an organization contributes to mitigating business-related risks and also creates new types of risks associated with its establishment. Businesses must prepare for, react to, and recover from unprecedented threats that might emerge in the years or decades that follow. This paper provides a comprehensive narrative review of risk management in information systems coupled with its application in fraud detection and continuity planning.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".