Fintech Meets Behavioral Finance
This is a retelling of Chapter 12 (Fintech) from “Behavioral Finance for Private Banking” by Thorsten Hens, Enrico G. De Giorgi, and Kremena K. Bachmann (Wiley, 2018).
This is the shortest chapter in the book. Only a few pages. But it asks one of the most important questions in modern finance: can robots replace human financial advisors?
A Short History of Fintech
Here’s the thing. Fintech is not new. People act like it appeared with smartphones and Silicon Valley startups. But financial technology has been around for decades.
When Harry Markowitz created modern portfolio theory in the 1950s, the math he chose was shaped by the computers available at that time. Mean-variance optimization is basically solving a system of linear equations. Friedrich Gauss figured out the algorithm for that back in the 18th century. So Markowitz picked a method that 1950s computers could actually handle.
The authors make an interesting point here. What if Markowitz had access to today’s computing power? Would he have proposed something completely different? We don’t know. But it’s a good question.
Then in the 1970s, the derivatives revolution happened. Black-Scholes formula for option pricing. And what made that possible? Pocket calculators. Seriously. The technology that fit in your pocket changed how Wall Street worked.
So fintech has always pushed finance forward. But the latest wave is different.
What Changed This Time
The big difference now is that technology is not just in the hands of asset managers. It’s in the hands of clients.
Everyone has a smartphone. Everyone has internet access. And that changes the relationship between advisor and client completely.
The authors walk through how technology now touches every step of the wealth management process:
Onboarding. You used to need a physical meeting to open a bank account. Show your passport, sign papers, shake hands. Now passports are electronically readable. Computers can recognize your face, voice, fingerprint, or iris. The whole process can happen without a human.
Financial planning. Identifying goals, putting values on them, ranking priorities. Software can help with all of this.
Risk profiling. Instead of filling out a boring questionnaire, clients can use experience sampling, where a computer shows you simulated investment outcomes and watches how you react. Some firms even use gamification to help clients understand their own risk tolerance.
Portfolio management. Investment styles can be programmed. Once your preferences are set, the system executes without anyone making emotional, last-minute decisions.
The Rise of Robo-Advisors
So here’s what happened. Someone looked at all those automated steps and asked: why do we need a human advisor at all?
Enter robo-advisors. Fully automated financial advisors with no humans involved.
The authors note that robo-advisors vary a lot in how smart they actually are. Some are basic. They give you a simple questionnaire, then put your money into a mix of ETFs. That’s it. Others are more advanced, using experience sampling techniques and offering active asset management.
But here’s the problem. At the time the book was written, none of the robo-advisors could properly assess what investment style a client actually prefers. They could figure out your risk tolerance (maybe), but not how you want your portfolio structured at a deeper level.
This is a gap. And it’s the same gap the authors have been pointing out throughout the entire book. Regulations like MiFID II require understanding the client’s preferences. Most robo-advisors fall short of that standard.
Humans vs. Robots: Who Wins?
Robo-advisors are definitely cheaper. No question about that. Lower fees, no office rent, no coffee budget.
But are they better? The authors are honest: we don’t know yet.
Here’s one important detail. Almost all robo-advisors launched after the 2008 financial crisis. So they’ve operated mostly during a long bull market. They haven’t been truly tested by a real crash.
Some traditional advisors argue that human advice matters most during a crisis. They call financial advising a “hand-holding business.” When markets drop 30% in a week, people panic. They want to sell everything. A human advisor can talk them down, remind them of the plan, keep them from making the worst mistake of their investing life.
Can a robot do that? Maybe. Maybe not. The race between humans and machines is still running.
Why Behavioral Finance Still Matters
This is the key takeaway of the chapter. Whether you use a human advisor or a robo-advisor, behavioral finance matters for both.
Knowing about biases like loss aversion, herding, and overconfidence is useful for humans and for the people programming robots. Understanding decision theory gives a solid foundation for the advisory process, no matter who (or what) is doing the advising.
The authors are clear: behavioral finance is not a human-only concern. It’s the foundation for good wealth management, period. If your robo-advisor ignores behavioral insights, it’s going to make the same mistakes that bad human advisors make. Just faster and at scale.
My Take
This chapter is short, almost like an afterword. But its message is important.
Technology will keep changing how financial advice is delivered. That’s not a question. The question is whether the people building these tools understand how humans actually make decisions. Because if they don’t, they’ll build beautiful, efficient systems that still fail their clients when it matters most.
The best outcome is probably not human vs. robot. It’s human plus robot, with behavioral finance built into both.
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