Triangulating Risk Profile and Risk Assessment: A Case Study of Implementing Enterprise Risk Management System
Bibliographic record
Abstract
Establishing an enterprise risk management (ERM) system is widely viewed as providing firms with the tools and processes needed to build resilience and expertise, enabling them to manage the consequences of crises that have led to the collapse of major firms across different industries globally. Intended for use in advanced accounting, auditing, and finance courses, this case study (of a true event) describes the development and implementation of an ERM system for a U.S. multinational nonprofit firm during the 2015–2021 period. The case study’s main learning objectives are several-fold. First, couched within the recent economic environment, it informs students on some of the more important academic and applied research on corporate risk management. Second, students will learn to analyze the content of a questionnaire designed to capture the integrated effects of the firm’s risk culture, risk structure, risk governance, and control for establishing its risk profile. Third, they will learn to create and apply multi-dimensional risk indices to measure and prioritize the firm’s risk exposures. Finally, the last learning outcome focuses on strategies to triangulate the firm’s overall risk profile and risk prioritization results to construct mitigation strategies that build resilience and create value through risk diversification, information signaling, the exploitation of natural hedges, and enhancing the board’s governing efficiency. The nonprofit nature of the firm in this case study introduces no methodological or conceptual constraints or limitations in applying the proposed risk management methodologies to for-profit or publicly traded firms.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| 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".