Sanlam Global Investment Solutions will launch the investment engine in March.
It uses multiple machine learning techniques to learn from large sets of data, “deduce evolving relationships and predict expected price with a high degree of accuracy”.
The system also contains an “AI risk manager which attributes weights intelligently across asset classes” with a goal to achieve a “certain minimum level of return while minimizing expected portfolio capital loss”.
SGIS has appointed London based A.I. Machines, a financial technology specialist to manage a number of investment mandates utilising their advanced Artificial Intelligence & Machine Learning investment engine.
Gideon Nell, head of global distribution for SGIS, said: “We have been on a quest to supply investors with a truly differentiated investment solution designed to eliminate human emotions, actively adapt to changing markets and help to materially improve the consistency of achieving investment goals with significantly reduced downside risk. We believe the AI capability ticks all these boxes. The absence of human input to make investment decisions continues to support our belief in behavioural finance.”
Cobus Kruger, CEO of SGIS, said: “The majority of industries are embracing artificial intelligence and machine learning to ensure their clients’ experience is the best it can be. However, the mainstream asset management industry is way behind the curve in implementing AI. Now is the time to challenge the status quo of investing. In order to succeed you need an investment strategy that adapts to deal with the new paradigm in markets.”
David Itzkovits – head of investments for SGIS, said: “The AI capability works by constantly adapting to changes in markets. We have leveraged a systematic investment strategy since 2010 in the form of Sanlam Managed Risk and this new AI capability is the natural evolution of that strategy. The AI investment engine derives its returns in a different way to other investments and should be viewed within a client’s overall investment strategy and as a diversifier of manager risk.”