Sanlam turns to AI to manage its Balanced UCITS fund
19 June 2017
Sanlam Global Investment Solutions has announced that the Sanlam Managed Risk UCITS fund is now being managed solely by Artificial Intelligence, which, the company says, makes it the first Balanced Fund in the world fully driven by AI.
Sanlam said the decision was made due to the unprecedented central bank participation over recent years which mean markets now move faster than ever before and investment strategies need to keep pace in order to provide investors with the optimal client experience.
The fund was previously managed by a systematic investment process, and will now be managed by an advanced Artificial Intelligence (AI) & Machine Learning (ML) investment engine.
Talking about the change David Itzkovits, head of Investments for SGIS, said: “The goal at SGIS was to find a solution that can adapt as quickly as the markets change. Our AIcapability does this by applying the latest in ML techniques which have multiple years of live operational experience and a real, impressive track record. The AI investment engine derives its decision making in a different way to other investments and should be viewed within a client’s overall investment strategy as a diversifier of existing human manager risk. Because in today’s world, it’s not man vs. machine, it’s man with machine vs. man without.”
AI & ML can be applied in investment management broadly in two ways:
1. Enhancement of a manager’s current investment process– by producing timelier asset allocation and stock allocation buy/sell signals on a portfolio of instruments chosen directly by the investment manager.
2. Total automation of the investment process – Meaning the AI selects the optimal portfolio allocation based on a pre-defined investment strategy with objectives, constraints and investable instruments.
The SMR fund utilises the latter.
Cobus Kruger, chief executive of SGIS, added: “The purpose of AI is to help humans more efficiently process and interpret the vast amounts of data. Machine Learning autonomously learns and adapts to new data without being programmed and at speeds that are far beyond human capacity. These two factors together should provide good value and help deliver better, more consistent outcomes for investors.”
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