Ortec Finance has unveiled GLASS PRISM, a next-generation Strategic Asset Allocation (SAA) optimisation tool designed specifically for insurers navigating volatile markets, tightening regulation, and increasingly complex investment objectives.
At the heart of GLASS PRISM is what Ortec Finance calls its Scenario-Based Machine Learning (SBML) methodology. Rather than relying on the simplified assumptions that underpin traditional optimisation approaches, the tool draws on thousands of simulated economic scenarios generated by Ortec Finance’s existing GLASS ALM platform.
These scenarios are used to train machine learning models that can optimise asset portfolios across a wide range of insurer-specific objectives — from solvency capital requirements and liquidity targets to dividend policies and surplus metrics.
Traditional portfolio optimisation tools, such as those built on mean-variance or CVaR frameworks, have long struggled to accommodate the full complexity of an insurer’s balance sheet. Constraints tend to be linear and allocation-based, objectives are narrow, and the scenario sets used are often static or limited in scope. GLASS PRISM is positioned as a direct answer to these shortcomings. The platform is capable of handling non-linear, regulator-defined constraints natively, without the approximations or workarounds that conventional tools typically require.
Because GLASS PRISM is fully integrated with the GLASS ALM platform, insurers can move directly from long-term balance sheet projections into AI-driven optimisation without switching systems. Once the machine learning models have been trained — a process that runs overnight — subsequent optimisations can be generated in minutes. This rapid turnaround enables insurers to update their SAA frequently and respond more agilely to shifting market conditions or regulatory changes.
Transparency is also a central feature of the offering. Every recommendation produced by GLASS PRISM is backed by the underlying GLASS analytics, giving investment teams the auditable trail they need to justify decisions to boards, regulators, and other stakeholders.
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