The Risk-Adjusted Intelligence Dividend: A Quantitative Framework for Measuring AI Return on Investment Integrating ISO 42001 and Regulatory Exposure

Battle Count: 50

The paper has limited direct relevance to quantitative trading. It addresses AI investment evaluation from a corporate governance and risk management perspective rather than trading strategy development. However, the risk quantification methods (Monte Carlo simulation, ALE calculations, probability distributions) and the concept of risk-adjusted returns share methodological parallels with quantitative finance. The framework could inform decisions about deploying AI in trading systems, but the paper does not address market microstructure, alpha generation, or trading-specific risk factors.

High implementation complexity due to: (1) requires cross-functional teams spanning finance, risk, technology, and legal; (2) demands mature data infrastructure for baseline measurement and ongoing tracking; (3) needs interdisciplinary analytical expertise in finance, statistics, and ML; (4) requires organizational governance changes including new review rhythms and accountability structures; (5) Monte Carlo simulation and probability modeling require specialized skills; (6) process mining and time-motion studies for baseline establishment; (7) ongoing quarterly recalculations and continuous monitoring; (8) integration with existing capital allocation and compliance processes.

This is a conceptual framework paper without empirical validation, code, or datasets. The methodology is described in sufficient detail for practitioners to implement, but there are no computational experiments, no open-source code, and no specific numerical results from case studies. Reproducibility depends on organizations implementing the framework with their own data. The framework references established methods (ALE, Monte Carlo) but does not provide a working implementation or validated examples.