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 highlight the growing role of machine learning in investment decision-making and the opportunities it presents for insurers and other institutional investors.
Click the link below to read the full article.
Title: The Role of Scenario-Based Machine Learning (SBML) in Asset Portfolio Optimization
By Ashish Doshi, UK Insurance Lead, Insurance Strategy
Summary
Institutional investors, particularly insurers, face an increasingly complex investment environment, balancing returns with risk, solvency, liquidity, regulatory requirements and changing market conditions.
Traditional portfolio optimization approaches can struggle to capture these competing objectives and the non-linear nature of today’s investment landscape. Technological advances are fast becoming the answer, especially technology using AI. Scenario-Based Machine Learning (SBML) offers a more advanced approach by combining stochastic scenarios with machine learning to assess a wide range of potential outcomes.
SBML enables investors to optimize portfolios against multiple objectives and constraints simultaneously, such as maximizing returns and dividend capacity while managing solvency capital, liquidity and downside risk. This can help create more efficient and tailored Strategic Asset Allocations (SAA).
This article examines how SBML can work alongside established Asset Liability Management (ALM) frameworks, enhancing existing processes rather than replacing them. By reducing reliance on manual trial-and-error analysis, it can help investors make faster, more informed decisions and build portfolios that are better aligned with their strategic and regulatory requirements.
Read the full article: https://talkfintech.com/guest-author/revolutionizing-investment-strategies-with-sbml
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