Introduction of a Corporate Security Risk Management System: The Experience of Poland
Bibliographic record
Abstract
To ensure the economic security of companies, it is necessary to introduce a risk management system based on the use of various tools, especially financial ones. The purpose of the article is to scientifically substantiate the paradigm of integration of the risk management mechanism into the system of economic security in companies on the basis of risk-oriented management. The main study method was an online survey of 50 Polish companies in January–April 2021 using a developed questionnaire consisting of 40 questions. According to the results of the expert survey, it is determined that regardless of the type of economic activity of the enterprise, the main goal of introducing risk-oriented management is to preserve assets and increase the efficiency of financial and economic processes. The introduction of risk-oriented management is perceived as a tool to increase the value of the company and ensure the achievement of strategic goals. Fraud is a significant risk to the state of economic security for modern enterprises. To prevent the fact of fraud, taking into account the specifics of the operation of companies, it is suggested to conduct an annual examination. As a result, the suggested procedure should include an audit (audit of financial statements, forensics, transition to international financial reporting standards, audit of systems and processes), assessment (assessment for audit and reporting in accordance with international financial reporting standards, risk management assessment in accordance with international standards, assessment of the effectiveness of economic security), tax analytics (identification of tax risks, analysis of compliance with tax legislation, tax audit), and a due diligence procedure for investment objects.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".