A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.

Model governance for insurance pricing — PRA SS1/23 validation reports, model risk management, risk tier scoring

Causal credit risk engine

Control-plane architecture for AI & agentic systems: governance as admission control, decision admissibility, and audit-grade evidence.

Model risk management — ModelCard, ModelInventory, RiskTierScorer, GovernanceReport

Model risk validation sandbox for market & credit risk (VaR, ES, EL, backtesting)

Regime-aware quant risk and market stability monitoring framework.

Defensible risk evidence for deployed machine learning models

Regime model stability diagnostics for SR 11-7 and Basel IV governance

Demonstrate how relying on a single metric can mislead model evaluation and expose hidden failures in machine learning systems.

React + TypeScript oversight hub for model criticality, release gating, evaluation drift, and executive AI risk visibility

React + TypeScript studio for model release gates, policy-sensitive approvals, evaluation drift, and AI governance review workflows.

Coordinate multiple LLMs in parallel for voting, debate, synthesis, critique, red teaming, and verification via MCP and Claude Code

Claude plugins for second-line financial-services work: GRC, regulatory change, AI/model risk, third-party risk, compliance testing, risk reporting, financial crime, consumer compliance — across banking, insurance, capital markets, and payments/fintech.

How portfolio diversification breaks down under stress correlation matrix analysis across COVID, 2022 rate shock, and 2025 tariff shock using Python and public market data.