Financial advisors can emphasize the crucial role they play in mitigating their clients’ biases and step in where new technology falls short.Parradee Kietsirikul/iStockPhoto / Getty Images
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The emergence of artificial intelligence (AI) tools in the financial arena has been both disruptive and promising. As machine learning, large language models and artificial neural networks enter the mainstream, a natural question is whether investors using applications built on these technologies might finally be able to avoid costly mistakes that arise from behavioural biases, perhaps rendering human financial advisors obsolete.
One reason AI-based advice will not dominate advisor guidance is due to the human coder’s imprint on AI tools. Algorithms are created by human programmers, and people cannot avoid imparting their own biases into the code they write. Whether consciously or unconsciously, programmers make choices about how to design software, how to assess financial risk, which financial data to select for training computer programs, and how to set automated thresholds for taking specific trading actions. As a result, algorithm-driven investment decisions inherently include more than a bit of human nature, for better or worse.
Another factor is that the financial data sets upon which algorithms are trained are themselves the outcome of human behaviour, including investors’ bad choices about when to buy or sell. Human emotions such as greed and fear lead to market bubbles and crashes. Reddit forum participants speculate on meme stocks, creating short squeezes and price surges. AI tools built on historical market data, with all their sentiment-driven frenzy, may perpetuate such events unwittingly.
Even if we somehow managed to create bias-free algorithms and trained them on market data that had been magically stripped of human fingerprints, investors using the AI would still occasionally give in to their worst instincts and hit the override button – especially during periods of extreme market volatility, which is when people tend to make their costliest financial mistakes.
The black-box syndrome poses an additional challenge. AI models can be complex and opaque, and investors tend to dislike receiving recommendations unless they understand the underlying rationale. Without transparency and context, many investors will not trust advice that emerges from AI.
Of course, there are some limited settings in which AI is being applied successfully to steer investors toward better outcomes. Robo-advisors match clients’ risk preferences with appropriately designed buy-and-hold portfolios. Some also use algorithms to monitor and rebalance portfolios, ensuring the asset allocation remains appropriate for a given investor over time.
But more generally, AI is unlikely to shield investors from the massive array of behavioural biases that make us all beautifully, imperfectly human.
The path forward for proactive financial advisors is to continue emphasizing the crucial role they play in mitigating their clients’ biases and to step in where new technology falls short. While AI can analyze huge volumes of data in virtually no time, it cannot eliminate our human nature, including our inclination toward herd mentality, our irrepressible overconfidence, our susceptibility to confirmation bias, and our aversion to realizing losses (“If I don’t sell this poorly performing stock and turn my paper loss into an actual loss, then I didn’t make a bad decision when I originally bought it”).
Advisors can act as a fuse between their clients’ impulses and the resulting decisions. They can tailor advice to clients’ personal values – a moral compass is still beyond the reach of today’s robots – and they can maintain their clients’ confidence and trust.
Technology simply cannot replace the insights, human touch and expertise advisors provide. Advisors who rise where AI falls short will continue to provide value to their clients throughout this period of technological change.
Lisa Kramer is a professor of finance at the University of Toronto.
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