An Artificial Intelligence Value at Risk Approach: metrics and models

Battle Count: 50

The paper has limited direct relevance to quantitative trading. However, it shares methodological foundations with financial risk management: the Value at Risk (VaR) concept, Monte Carlo simulation, and the FAIR model are all used in quantitative finance. The AI-VaR framework could theoretically be adapted for AI-driven trading systems risk assessment. The paper's discussion of aleatoric vs. epistemic uncertainty, risk calibration, and prediction intervals has conceptual parallels with quantitative trading risk management. The primary focus is on regulatory compliance and fundamental rights rather than market risk.

The conceptual framework requires understanding of FAIR model, Monte Carlo simulation, PERT formula, risk ontologies, and multiple AI/ML metrics. The basic examples are simple (logistic regression, linear regression), but the full integration of five risk dimensions into a unified AI-VaR model requires significant domain expertise in both AI/ML and risk management. The paper itself notes it requires 'basic knowledge of risk management, quantifying uncertainty, the FAIR model, machine learning, large language models and AI context engineering.' Implementation would require custom software development for the risk ontologies and Monte Carlo simulations.

The paper provides synthetic examples and basic datasets (5-person dataset, 20-candidate HR dataset, 10-patient cancer dataset, 10 administrative fines dataset) with illustrative figures. However, no code, specific software implementations, or detailed parameter settings are provided. The Monte Carlo simulation (1000 iterations) and PERT formula are described but not fully reproducible without additional implementation details. The risk ontologies are presented conceptually rather than as executable models.