Banking: More personalised services could be delivered with AI

This article first appeared in Personal Wealth, The Edge Malaysia Weekly, on December 24, 2018 - December 30, 2018.

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. > Clara Durodié. Photo by Mohd Izwan Mohd Nazam/The Edge

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Artificial intelligence (AI) will be a major disruptive force when it comes to client interfacing as it could provide the kind of “personalisation at scale” that banks and financial institutions have been targeting to do for years, says Cognitive Finance Group executive chair Clara Durodié.

“Personalisation — that is, understanding and accurately predicting the client’s needs — is a core component of scaling the business. In a data-driven industry [such as financial services], there should not be any trade-offs. Both can be delivered effectively at the same time, thanks to AI,” she says.

Durodié was speaking to Personal Wealth on the sidelines of the recent SCxSC Fintech Conference 2018. Cognitive Finance Group is an advisory and investment firm that specialises in AI solutions for the financial services industry.

She cites DBS Bank in Singapore as an example. In 2016, it pioneered the first mobile-only bank in India. Known as “digibank by DBS”, the service is touted as a branchless, cost-effective, AI-driven banking service. It provides most of the banking, payment and investment services of a regular bank, but at a fraction of the cost.

digibank saw a million users register for its services in its first year, all of whom did so without having to step into a physical branch. The mobile-only bank projects five million customers by 2021, according to its website.

The successful deployment of digibank has been attributed to its advanced, client-facing conversational AI platform. This is how it interacts with its fast growing user base. In fact, the AI assistant handles 82% of customer requests and inquiries, all without the intervention of human support staff.

Durodié believes AI in wealth management is capable of even more. “As part of scoping my PhD research, I focused on the intersection of neuroscience, AI and wealth management. I looked at how episodic memory informs how we save and invest. My area of interest was researching how AI could improve an investor’s poor decision-making, which is sometimes marked by early childhood experiences.”

Good financial practices start at childhood and children subconsciously develop these practices by observing how their parents managed money, Durodié contends. “Not everybody had good saving and investment practices reinforced in their subconscious when they were young,” she points out.

“For example, when you are about to buy your 10th pair of shoes in three months, an AI-based assistant could prompt you at the point of payment. It could remind you that you spent US$1,500 on shoes over the last three months. This little nudge is enough to influence a person to make an entirely different and more responsible choice. We can use AI to empower people in building their wealth.”

Taken even further, an AI-based financial assistant could be expanded to help people with their insurance plans. “This is something else that people do not typically like to do, reviewing their insurance plans. You could, as a user, empower the AI algorithm to track better quality plans elsewhere,” says Durodié.

Having generated all these savings, the AI assistant could prompt the user to consider a selection of investment vehicles in which to park that money. Managing a portfolio is yet another pain point that the user experiences, says Durodié.

“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.

Agent-based models refer to computer models that set out to capture the behaviour of individuals, groups or businesses (also known as agents) within a particular environment. They are seen to be more intuitive than purely mathematical or statistical models, according to a lecture by Dr Andrew Evans, a senior lecturer at the University of Leeds’ School of Geography.

A Harvard Business Review article published last month noted that in the case of financial risk, agent-based models can be designed to include very granular data. As the rules assigned to these models become more refined, the outcomes of these models will gradually become very accurate. It could be possible to deploy AI to control the rules by which these models operate.

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?”

By empowering financial institutions to do more with less, the very functions of finance will change overtime, she says. “Those who understand the abilities of AI to improve these functions will be able to optimise their financial resources. The more money one is able to manage optimally, the wiser one’s investment decisions will become, enabling the organisation to grow quickly, all the while providing unprecedented value to its users [in the form of ultra-low costs or superior portfolio performance].”