Ortec Finance has launched GLASS PRISM, an artificial intelligence-powered Strategic Asset Allocation (SAA) tool built to help insurers optimise their investment portfolios in ways that traditional methods have been unable to deliver.
The platform is underpinned by a proprietary Scenario-Based Machine Learning (SBML) methodology, which trains AI models using thousands of simulated economic scenarios produced by Ortec Finance’s GLASS ALM platform. This approach allows GLASS PRISM to optimise portfolios against objectives that are directly tied to an insurer’s business performance — including solvency and capital requirements, liquidity metrics, dividend targets, and surplus goals — rather than defaulting to the generic return-and-risk frameworks that have long defined the industry standard.
The limitations of conventional optimisation tools are well documented in the insurance sector. Mean-variance and CVaR-based approaches tend to handle constraints in a simplified, linear fashion, operate with restricted scenario sets, and are ill-suited to the multi-dimensional objectives that insurers must balance.
GLASS PRISM is designed to eliminate that compromise, incorporating non-linear and regulator-defined constraints directly into its modelling without oversimplification.
The tool sits within Ortec Finance’s broader GLASS ecosystem, fully integrated with the firm’s existing ALM and balance sheet modelling platform. That integration means insurers can transition smoothly from running long-term liability projections to executing AI-driven portfolio optimisations within a single environment. The machine learning models are trained overnight, after which optimisation results can be produced in minutes — a considerable improvement over the iterative, time-consuming processes that characterise many existing solutions.
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