A New Approach to Financial Technology That Makes Everyone Better Off
Have you ever made a financial decision that didn’t go well in hindsight—like mistaking news for investment advice and acting on it, trading too often, or avoiding investing completely?
Don’t worry, you’re not alone.
We’ve all made choices based on emotions, habits, nudges and/or impulse that ended up becoming costly mistakes.
Today, new technology is improving the way we make investing decisions by helping investors avoid both obvious and difficult-to-detect mistakes and identifying opportunities. Behavioral financial technology (behavioral fintech) is the culmination of the best behavioral economics research coupled with the benefits of digital technology, directed at practical financial problems we all face. This problem-solving technology is bringing about a new type of investor experience that can lead to better results.
What is behavioral fintech, how does it work, how can it make a meaningful difference in your life, and why is it becoming a meaningful investment game-changer?
What do you mean by “behavioral”?
The term “behavioral” comes from a major shift that occurred in economics in the 1980’s and 90’s that evolved the field from a “neoclassical” and heavily theory-based area of study to one with more practical applications. This new approach moved away from studying what perfectly rational “agents” would and should do, and towards looking at how normal, flawed, and busy people actually behave in real situations under real constraints with real consequences.
This new subfield of “behavioral economics” shifted research towards evidence-based topics and extended the types of questions taken on by academics to consider practical problems people face, such as why some people unnecessarily pay astronomically-high interest rates or why crime rates drop in some cities and not others. One of the major advances in the field is known as Prospect Theory, an elegant mathematical model that explains why people avoid taking risks in some situations and conversely take large risks in other situations.
In step with economics, behavioral finance, a subfield of economics (called “financial economics”) also grew and expanded in scope and started addressing practical problems, such as why retail investors lose money when investing on their own, why people avoid the stock market, and whether markets are perfectly efficient.
The beauty of these findings is that they emerge across areas of life, not just in money matters. After all, our brain uses its same features for a variety of tasks. So it shouldn’t be a huge surprise that patterns observed in interpersonal relationships also show up in our investing behavior.
Behavioral economics is more useful than “traditional” theory because scientists can identify what factors weigh into our decisions, better positioning us to solve our biggest problems. One of the practical insights of behavioral economics is that by planning for irrationalities and systematic errors in decision-making (instead of hoping they simply go away) we can make better decisions in the ‘heat of the moment’.
All together now: Behavioral Fintech
Academic research has shown us the problems people face as financial decision makers include missing out on reliable investment opportunities, delegating responsibility to the wrong advisors, and taking unnecessary risks.
Behavioral fintech actively solves these problems.
Technology and society changed rapidly and continue to do so, but our brains are essentially the same as when humans lived in tribal settings with unpredictable and dangerous environments. This is problematic today because our innate ways of dealing with risk, uncertainty, and complexity are managed largely by cognitive shortcuts (known as “heuristics”) that worked ‘in the wild’ to keep us alive but lead to meaningful mistakes today.
The biases that drive our biggest financial mistakes are often exploited by companies, generally leading to many consumers being worse off. This is due in part to how our brains process information and operate when we make economic decisions. It is also the side-effect of technological innovations, which are sharp double-edged swords with convenience on one side, and the potential for exploitation on the other.
The credit card, invented in 1946 when John C. Biggins introduced the “Charg-It”, then popularized by Diners’ Club in 1950 is the perfect example: the technology that allows to consume now and pay later perfectly exploits humans’ preferences for immediate gratification and delayed displeasure (known as “beta delta preferences”). Fast forward to Q4 of 2024 and consumer debt is now $17.3 trillion in the US alone, and climbing. This pattern is also observed in online trading, discussed below.
In order to help actual people better navigate our complex financial decisions, a new form of technology called behavioral fintech is emerging. This is new technology that facilitates financial decisions—often driven by emotions, habits, or biases—with a consumer-centric approach to help people get better outcomes.
In order for financial technology to be considered behavioral fintech, we propose the following criteria:
Customer-Centricity
The first pillar of behavioral fintech is that it prioritizes the customer’s objectives. This means that the net benefit is designed to be gained by the person using it, even if at the cost of firm short-term objectives. One could argue that all fintech is behavioral fintech in the same way that all economics is behavioral, but the differentiator here is that in order to be behavioral fintech proper, the technology must effectively solve a customer problem and provide a net benefit to the customer.
Today’s online investing platforms allow people to make quick investment decisions at “no cost” and reinforce the decision to trade by displaying digital confetti to celebrate this user behavior. The human brain is not optimized to make correct rapid financial decisions, and evidence suggests that people need extensive training to generate investing profits consistently.
People trade poorly at baseline, and even worse as online trading became available years before gamification. Gamification used by investing platforms further exploits human vulnerability to dopamine-driven novelty seeking, a feature that does not prioritize optimal investing decisions and wealth accumulation (i.e., which is presumably most investors’ primary goal). The impact of gamification is beginning to be studied, and the impact of the technology is partly driven by self-selection, and this is discussed in more depth in this article called “Nudging to trade”.
The general public is not likely going to revert to using phone calls or in-person meetings to make investment decisions. People who might have adopted a proper investment process or sought help from a financial advisor to make investment decisions are likely using trading platforms for their investments. This is unfortunate because these platforms minimize the focus on making smart investment decisions and maximize the casino-like atmosphere that rewards quick and frequent action.
For investment technology to be considered behavioral fintech it must first suit customers’ stated needs. If the customer’s primary objective is wealth creation, then gamified trading platforms are demonstrably not the proper way to accomplish this objective. For people who want to increase their portfolio performance (versus engaging primarily for enjoying the game of trading), investors must create their own investing workflow.
In order for an effective, repeatable investment process to work, the investor user experience needs to incorporate behavioral technology that addresses the problems that rapid-fire trading technology has created along with the standard challenges of being a successful investor over long periods.
If enjoyment of trading is the primary goal and profit is not a meaningful consideration for a particular “persona”, then perhaps a structured investment process or behavioral analytics would not find “product-market fit” in this segment.
Hyper Customization
One of the primary contributions of behavioral fintech is the ability to leverage data to customize the experience and generate a correct diagnosis. This means that each person is unique and needs to be understood in their own context. The value of correctly identifying problematic behaviors is that each of us gets the opportunity to improve upon habits that we don’t know we have.
This powerful opportunity is enabled by behavioral economics research that now functions as live algorithms that work for the investor. For example, behavioral algorithms can “understand” trading behavior and communicate insights to the investor in an understandable way. Without these insights, we each run the risk of making the same mistakes over and over and not knowing why.
For example, have you ever bought more of a stock that was losing value, thinking it would “get back to even”? This is a common emotional reaction known as “loss chasing” that often leads to bad financial outcomes. Behavioral fintech can identify when you’re engaging in loss chasing and help you avoid them before you commit more money to a losing strategy.
Despite the prevalence of similar financial behaviors across people, each person needs to be understood uniquely and their financial information analyzed in their particular context. This means that one-size-fits all approaches should not be deployed as they might fail to meet the highest priority needs faced by the individual at a particular point in time.
For example, a household that seems to be “underinvesting” in the stock market relative to their income might be coping with a temporary financial challenge (such as an unpaid maternity leave) that requires all available funds to resolve. Without this added information, the wrong advice might be given and incorrect actions deployed.
Some investors have a knack for buying under-priced stocks and are known as “dip buyers” as they generate significant profits from this strategy. On the other hand, investors who buy companies with downwards trajectories without recovery (also known as “catching a falling knife”) destroy their wealth by engaging in the same behavior. Being able to ascertain which camp an investor falls into can help craft strategic feedback to help that particular person.
Transparency
Without the “full picture” we often arrive at the wrong conclusions. This is why data and analytics transparency are critical from a customer perspective so that people make informed choices.
While it is sometimes true that too much information leads to poor decisions, it is still a feature of behavioral fintech to provide information in such a way that optimal decisions can be made in complex scenarios. In fact, this is the primary goal.
While “nudging” became wildly popular and entire government ‘nudge units’ were created to craft decision architecture to encourage better choices, behavioral fintech goes a step further. Behavioral fintech seeks to not “lead” people to make particular choices but to present them with likely, evidence-based outcomes garnered from all available data. This is less of a “paternalistic” method, and more of a fact-based approach that empowers individuals to make optimal decisions with better understanding of the costs and benefits without imposing the optimal choice.
By surfacing data and associated consequences at the right time, investors can benefit from hyper customization enabled by data transparency and make more informed decisions. This is only possible to do with a dynamic, data-driven approach deployed to solve specific problems for particular people at specific moments in time.
Evidence-Based Approach
To bring it all together, the critical factor is that outcomes are clearly identified and measured along with other relevant data so that there is strong evidence that the technology has the intended, positive impact.
Debiasing and behavioral interventions do not always work out as planned, but that doesn’t mean that they should be abandoned completely. By understanding what insights drive optimal behavior and what might exacerbate mistakes, behavioral fintech can evolve as a ‘living laboratory’ and continually iterate to generate better, evidence-based outcomes.
There are a few reliable approaches to applying the scientific method to help people make better decisions. For example, measuring impact of a particular behavioral fintech system can be done with randomized controlled trials (RCTs) or comparing before and after treatment. The scientific method is feasible because portfolio decisions can be tracked carefully and the impact of behavioral fintech can be clearly identified.
We developed behavioral analytics as a service so that investors can readily see their own investment biases on our platform. This “upside analysis” is generated by pulling an investor’s historical trade data, parsing it for trade decisions, then quantifying the impact of particular biases on portfolio performance.
The solution to detrimental investing behaviors? A guided investment workflow that de-biases investors and leads to consistently better outcomes called SmartTrade. In our first pilot study in 2024 testing the impact of SmartTrade on investment performance, we found that the treatment group performed 20% better than the placebo group on an annualized basis.
Despite this remarkable impact, we also learned that the effort required to use the technology was effortful and adoption might have been hampered because of the cognitive requirements themselves.
Since then, we have initiated larger-scale studies with top universities and investment platforms to ensure that behavioral fintech lives up to its promise and delivers meaningful economic value to those who use it. If you’re interested in participating in a study please contact us.
Charting a New Course for Fintech
Innovation in other areas of science and technology has enabled the invention and deployment of intelligent tools and gadgets that can help people in powerful ways. Behavioral fintech’s goal is to help investors overcome the odds that are stacked against them by serving their wealth accumulation needs. Behavioral fintech is the antidote to both innate biases and bad technology.
By using various powerful technologies like artificial intelligence (AI), Decision Support Systems (DSS), and leveraging actionable research in mathematics, economics, psychology and neuroscience, behavioral fintech helps people recognize patterns in their behavior and supports them in making better financial choices. This not only helps investors, but also financial advisors.
Equally important is defining that behavioral fintech does not exploit biases, weaknesses, and/or limited attention.
This will not only enable the opportunity to increase investors’ wealth who adopt behavioral fintech, but it stands to bring more investors into financial markets. One of the biggest takeaways from our internal product testing for our investing decision support system and trade journal (SmartTrade) is that new investors consistently said that behavioral fintech felt like it provided “guardrails” and that people felt “more confident” in starting to invest because of it.
In order for technology to be considered “behavioral fintech” it must meet these admittedly difficult criteria. But this is what is required if we want to build an inclusive investment community that allows a greater number of people to participate in financial markets, benefitting from more bespoke solutions tailored to their individual needs. If we can improve investment outcomes, that should elicit broader participation in financial markets, which should result in improved liquidity, and more stable markets.
As we embark upon the next generation of financial technology, it’s important that we move past “faster and cheaper” as the goal, and reorient our focus on creating better outcomes for all.