Machine learning transforms asset allocation for investors

Machine learning is reshaping strategic asset allocation, challenging the long-standing mean variance framework used by institutional investors.

While effective in simpler settings, the traditional approach often relies on manual iteration to balance risk and return across complex portfolios.

Ashish Doshi of Ortec Finance highlights how data-driven optimisation is enabling faster and more direct investment decision-making.

Ortec Finance’s Glass Prism tool applies scenario-based machine learning to portfolio construction, allowing investors to optimise against a wide range of objectives.

Ortec Finance notes that the system reduces reliance on trial and error and extends into insurance applications such as solvency capital optimisation and balance sheet management.

According to Ashish,  it improves speed, precision and robustness in portfolio generation.

As market conditions grow more complex, institutional investors are increasingly adopting machine learning tools to enhance risk management and long-term planning.

The shift reflects a broader evolution in investment strategy, where optimisation is becoming more adaptive, scalable and objective-driven.

Ashish suggests this marks a structural change in how portfolios are designed and evaluated.