Following the launch of GLASS PRISM, Ortec Finance attracted significant media attention, with industry publications exploring how the solution is transforming portfolio optimization for institutional investors.
The article below highlights how machine learning is transforming investment strategies for insurance asset managers.
Click the link below to read the full article.
Financial Investigator
Title: Machine Learning Transforms Insurers' Portfolio Optimization
Written by Ashish Doshi, this article explores how machine learning is transforming portfolio optimization for insurers, helping them navigate increasingly complex investment, regulatory and capital management challenges.
Summary: The article examines why traditional portfolio optimization techniques are becoming less effective in an environment shaped by economic uncertainty, geopolitical risks and evolving regulations. It explains how scenario-based machine learning (SBML) provides a more advanced, data-driven approach, allowing insurers to optimize across multiple objectives simultaneously, including investment returns, solvency capital requirements, liquidity and dividend outcomes.
The article also highlights how SBML enables insurers to model real-world scenarios, capture non-linear relationships and build portfolios that better reflect the complexities of today's investment landscape. By moving beyond traditional optimization methods, insurers can make more informed strategic asset allocation decisions while improving resilience in an increasingly uncertain market.
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