Data & AI
AI & Quantitative Risk Models
Machine-learning and AI models applied to credit, insurance, and financial risk — from gradient-boosting PDs and severity models to LLM-assisted risk assessment — with governance, interpretability, and model-risk controls built in from day one.
Outcomes you can expect
- AI models with lineage, monitoring, and explainability from day one
- A governance layer aligned to SR 11-7 and EU AI Act expectations
- Faster model iteration without piling up hidden model risk
Typical engagements
- ML models for PD, LGD, severity, fraud, and pricing
- Explainability layers (SHAP, PDP, surrogate models) and drift monitoring
- AI/ML model risk framework and EU AI Act readiness
- LLM applications for underwriting, KYC, and risk review
Related reading
Predictive Modeling in Insurance
Predictive modeling has quietly reshaped insurance pricing. Here's what actually works, what to watch out for, and how to keep models explainable to regulators.
Read the articleOther services
Financial Risk
Financial Risk & ALM
IRRBB, liquidity, and asset-liability management for banks and treasuries.
Financial Risk
Credit Risk & IFRS 9
PD, LGD, EAD and IFRS 9 ECL models — designed, calibrated, and validated.
Financial Risk
Model Validation & Model Risk
Independent validation of pricing, credit, capital, and AI models.
Insurance & Actuarial
Insurance / Actuarial & Solvency II
Solvency II pillar work, ORSA, technical provisions, and reserving for insurers.
Emerging Risk
Climate & Catastrophe Risk
Physical and transition climate risk, and catastrophe frequency-severity models.
Emerging Risk
Geopolitical & War Risk
Sovereign, war, sanctions, and supply-chain risk translated into balance-sheet numbers.