Banking: More personalised services could be delivered with AI

“Retail investors are prone to two key mistakes. First, they tend to buy at the peak and sell at the bottom because these decisions are heavily driven by emotions. Second, people tend not to review their portfolios, particularly if they are already underperforming. This is another problematic behavioural trait that AI can help improve. We have a tendency to put off rebalancing our portfolios until it is too late,” she adds.

“A user could grant some level of autonomy to the AI assistant to invest objectively, and without emotion, on the user’s behalf. That said, ultimate control remains with the human and the AI assistant constantly keeps him in the loop on prospective investment decisions.”

Far from just the client-facing interactions, AI has the ability to augment a fund manager’s investment decision-making. AI will help finance professionals refine data points and catch patterns that they would not have otherwise detected on their own.

“By combining agent-based modelling with AI, fund managers or banking executives are able to map out a variety of financial, investment and lending scenarios, possibly even in real time,” says Durodié.

Even without resorting to advanced agent-based models, deploying some form of AI — for instance, machine learning in a forecasting and budgeting function within a hypothetical, global financial institution — could yield significant efficiency gains, says Durodié. “Suppose this bank is opening a Southeast Asian office in Kuala Lumpur. Typically, the headquarters would convene its leadership at the end of the year to plan its operational needs for the next 12 months. Then, headquarters would allocate budgets to each of its offices.

“With AI, this function can be conducted in near real time and with far greater efficiency. AI models could project business growth and its associated costs for the new Kuala Lumpur office, taking into account Malaysia’s very unique set of [economic, social and financial] circumstances. These models would then provide a much more targeted and personalised budget allocation for that particular office, over a three-month period, for instance. After all, given how everything moves so quickly, what 12-month predictions persist with absolutely no changes?”

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