Ortec Finance has unveiled GLASS PRISM, a next-generation strategic asset allocation (SAA) optimisation tool designed specifically for insurance asset managers.
Built on what the firm calls its Scenario-Based Machine Learning (SBML) methodology, the platform aims to help asset managers deliver more sophisticated services to their insurance clients while driving growth in assets under management.
The tool comes at a time when insurance asset managers are navigating increasingly volatile markets, evolving regulatory frameworks and multi-dimensional investment objectives. Traditional optimisation methods, which typically focus narrowly on return and risk, are proving insufficient for the complexity of modern insurer balance sheets. GLASS PRISM is designed to address this gap by enabling optimisation across a much broader set of objectives and constraints.
These include solvency and capital requirements — encompassing complex solvency capital requirement (SCR) rules — as well as liquidity management, dividend targets and surplus metrics. Crucially, the tool can optimise against any metric generated by an asset-liability management (ALM) platform, offering considerable flexibility.
GLASS PRISM is fully integrated with GLASS, Ortec Finance’s existing ALM and balance sheet modelling platform. The integration means that thousands of stochastic economic scenarios generated by GLASS are used to train the machine learning models that power GLASS PRISM’s optimisation engine. According to the firm, this ensures every portfolio recommendation reflects realistic market dynamics, liability interactions and regulatory constraints, rather than relying on static or simplified assumptions.
One of the platform’s headline capabilities is its handling of non-linear constraints — something that conventional mean-variance or conditional value-at-risk (CVaR) optimisers have historically struggled with. Rather than approximating regulatory constraints, GLASS PRISM models them directly, allowing asset managers to work within the precise parameters that regulators and boards require.
Once the machine learning models are trained overnight, subsequent optimisations can be generated in minutes. This rapid turnaround enables asset managers to update their strategic asset allocations far more frequently than traditional approaches allow, making the tool particularly well-suited to fast-moving market conditions.
Transparency is another key feature. Every recommendation produced by GLASS PRISM is backed by GLASS analytics, providing the audit trail needed for regulatory reporting and board-level decision-making.
From a competitive standpoint, Ortec Finance argues that GLASS PRISM can unlock portfolio configurations that were previously undiscoverable using conventional tools.
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