AI Governance for Institutional Readiness in Finance

Battle Count: 86

Highly relevant to quantitative trading. The paper directly addresses governance of agentic AI systems in asset management, including LLM-embedding trading strategies (MSRR with Sharpe ratios exceeding 3), policy drift detection via regret-covariance decomposition, kill-switch architecture, vendor model dependency risks, and systemic crowding risk. It provides a concrete 90-day implementation roadmap for trading desks and addresses the specific failure modes (reward hacking, compositional risk, cross-institutional correlation) that quantitative trading firms face when deploying continuously-retrained strategies. The paper also extends governance to payments/fraud decision-making, broadening applicability beyond pure trading.

Moderate complexity. The paper provides a structured 90-day implementation roadmap with two parallel tracks (Trading and Payments). The regret-covariance monitor is computable from observed data alone (no access to model internals required), making it implementable even for vendor-supplied strategies. However, full implementation requires: (1) inventory of all agentic systems, (2) calibration of monitoring parameters against validated regimes, (3) vendor model-version attestation infrastructure, (4) interface contracts with exclusion lists, (5) inner-confidence computation (requires model internals), (6) bounded action authority ceilings, (7) consortium disclosure mechanisms, and (8) integration with existing regulatory compliance (Reg E, BSA/AML, PSD2/PSD3). The framework is designed for incremental adoption rather than all-at-once deployment.

Synthetic examples (regret-covariance monitor, crowding simulation) are fully reproducible with explicitly stated parameters (W, gamma, k, T=500, 30 seeds). Code and data for numerical examples are stated as generated by the code itself. Form ADV data is publicly available from SEC's Investment Adviser Public Disclosure database. However, the survey data is self-selected LinkedIn poll with no individual-level responses retained. The framework is a governance proposal rather than a trained model, so reproducibility applies to the illustrative computations rather than model training.