Ortec Finance’s Scenario-Based Machine Learning (SBML) combines AI with stochastic ALM modelling in our GLASS platform to help insurers meet real-world objectives faster and more precisely.
SBML naturally accounts for capital, solvency, ESG, and liquidity constraints—delivering portfolios that perform better under real-world conditions.
Featured Resources
1. Case Study: Balancing Risk, Return & Liquidity in a US Insurance Portfolio
Discover how Ortec Finance and AllianceBernstein used Scenario-Based Machine Learning (SBML) to optimize a US insurance portfolio across risk, return, and liquidity. This case study shows how SBML overcomes the limitations of traditional methods—handling complex regulatory capital metrics and multi-objective optimization with ease—to deliver more efficient, data-driven portfolio decisions.
See how Ortec Finance and AllianceBernstein used SBML to optimize an insurance portfolio across risk, return, and liquidity—achieving results traditional methods can’t match.
2. Optimizing Insurance Portfolios with SBML: Ortec Finance & M&G Investments
See how M&G Investments partnered with Ortec Finance to pilot SBML within GLASS—optimizing portfolios under complex regulatory constraints and uncovering more efficient results than traditional methods.
3. Unlock the Next Dimension of Portfolio Optimization
Discover how 3D SBML optimization, developed with M&G Investments, delivers superior portfolios by balancing multiple insurer objectives while maintaining full transparency.
4. How AI Can Help Manage Insurance Portfolios
Learn how AI-driven scenario analysis helps insurers uncover high-performing portfolios traditional methods miss—combining realism, speed, and explainability.
Why SBML
- Handles complex, non-linear regulatory constraints
- Optimizes for insurer-specific metrics
- Enables rapid iteration and transparent decision-making
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