model-risk

Open-source model inventory & governance. Discovers every model, rule, and pipeline across all your platforms as one immutable, agent-queryable graph — git for models.

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.

Reproducible benchmark of classical, neural and hybrid mortality forecasting models with actuarial evaluation.

Reproducible R study of motor insurance pricing on freMTPL2, with separate reserving and excess-of-loss simulation examples.

Reproducible benchmark of classical, statistical, machine-learning and neural claims-reserving methods.

Model-agnostic evaluation, uncertainty and reporting for actuarial predictive models.

Governance patterns for autonomous AI agents in regulated financial services — DEFCON state machine, Sovereign Veto, Audit Chain, EU AI Act mapping

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

Universal Analytics Engine: a dataset-agnostic BI platform with an embedded model-risk governance layer (PII detection, Canada AIA impact scoring, proxy-bias flags, integrity scorecard) designed around OSFI E-23. Governance architecture mine; Python/Streamlit AI-assisted. Live demo in the About link.

A benchmark for how AI models fulfill legal duties under pressure

Evidence-based evaluation methodology for coding-agent configurations: statistics, evidence contracts, and fail-closed reporting with synthetic worked examples. No real-model performance claims.

Point-in-time US equity research system for leakage-safe walk-forward ML validation, model-risk diagnostics, and failure attribution.

Calibrated probabilistic market forecasting—from point-in-time data to decision-readiness evidence.

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

Independent validation framework for CCP-style initial margin models, including VaR, margin add-ons, backtesting, stress testing, sensitivity analysis, procyclicality monitoring, and model-risk governance.

C-DAG: replayable causal audit traces for high-risk financial AI decisions.

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.

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

The first public LLM benchmark for Canadian financial regulatory compliance. Covers OSFI E-23, FINTRAC, B-20, IFRS 9, Basel III, PIPEDA, and CASL.

Auditable Qlib factor research validation, execution backtesting, and model-risk governance case study