Legacy Never Lasts: Inspiring Innovation in Fintech

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Jun 18, 2024

In the last article in our series on the unfulfilled promise of behavioral science in fintech, we explored a few of the many reasons we still haven’t seen the results we were hoping for from applying behavioral science to money-related apps.

The good news is that, even though behaviorally informed features might not be everywhere yet (or even in any of the five banking or money-related apps I currently have on my phone), there are individuals and organizations actively working on this. This sometimes takes the form of applied research and innovation firms such as ours, working directly with partners on bespoke ways of bridging the gap between a user's financial well-being goals, their socioeconomic position, and their underlying psychological barriers and drivers. 

Other companies take more of a platform approach, offering a productized solution integrated into an already existing product—think of your typical online banking app that has various new features added to it. There are actually quite a few of these, such as MoneyThor with a strong presence in Asia, which focuses on driving personalization and engagement, or Doconomy, a European platform creating a positive impact through improving climate literacy and introducing sustainable banking offerings. (Fun fact: After publishing the previous article, someone from Doconomy reached out to me on LinkedIn, which is how I came to find out about them!) 

So if there are already companies working on this, how come these offerings aren’t more ubiquitous? 

Financial institutions are still operating legacy tech stacks

Research from the Digital Banking Report in 2018 found that 94% of banking providers couldn’t personalize marketing or CX, struggling to tailor their services and communications to the individual needs and behaviors of their customers. For example, personalization in banking apps could involve sending targeted financial advice based on a customer's spending patterns, offering customized product recommendations, or providing timely reminders for savings goals. Although the report is from six years ago, and recent data shows that banks have made some progress in bringing more customized experiences to customers since then, the reality is that many of these organizations continue to struggle due to the many legacy systems still in place. Fast forward to 2022, and only about 14% of these institutions were able to offer contextually relevant experiences.

Behavioral Science, Democratized

We make 35,000 decisions each day, often in environments that aren’t conducive to making sound choices. 

At TDL, we work with organizations in the public and private sectors—from new startups, to governments, to established players like the Gates Foundation—to debias decision-making and create better outcomes for everyone.

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What are legacy systems?

Legacy systems are outdated computing systems or applications that are still in use despite being superseded by newer technologies. Usually, legacy systems are difficult to update or integrate with modern solutions.

You might be inclined to dismiss what seem like “outdated” reports, but our own experience with these types of organizations at TDL has shown us that this data still captures our current reality. In our mandates with financial institutions, we’ve been tempted to deploy interventions via simple channels such as emails, only to realize that these are, for the insurance company or bank we’re working with, still considered “cutting edge.” They simply did not have the ability to even change the email message they were sending out to new customers after they had submitted a request for a new financial product. Their system upgrade granting them the ability to do so was always 2-3 years away, so the only vector for personalization was… the agent that was calling them back after they completed the form!

Unfortunately, this is a reality that many large companies, not only banks and financial institutions, have to confront. Moving away from legacy systems that are limited in capabilities often involves multi year-long projects that require significant capital investments. These investments are slow to materialize until the competitive pressures are already upon them, by which point it’s already starting to have a material impact on their bottom line.

A word of caution for our international readers: having worked across multiple geographies, we realize that these pressures and realities can be different around the world. For example, in Canada, the banking system is tightly regulated by the government, and open banking still hasn’t taken off. As a result, the competitive pressures on incumbents are not the same as in the European landscape, where regulations have mandated a more substantial open banking environment. In contrast, the US has a more competitive environment where startups can acquire a banking license much more easily by partnering up with local banks.

Can anything be done?

We’ve witnessed projects where these limitations became significant roadblocks to integrating behavioral science effectively. For example, some innovation firms would come up with ambitious projects that involve revamping the entire customer journey for a large bank, with elaborate designs, well-thought-out flows, buy-in from senior stakeholders—only to realize that pretty much all of it is absolutely unimplementable without first transitioning away from legacy systems. So what happens in these cases? Five years down the line, and none of what they came up with was implemented… 

This is where firms that are trying to help these institutions integrate behavioral science in their products and services need to show a highly developed ability to adapt, taking a very pragmatic approach in deploying innovation despite any existing limitations. The ambitious initiative of revamping the customer journey that I mentioned above did not see the light of day even years later, but TDL’s subsequent involvement in the same organization delivered tangible results by using the only available vector at our disposal at that time: the agents. By revamping the agent scripts—a very low-tech and “not sexy” method—we managed to increase transparency, authenticity in customer interactions, as well as increase profitability by millions of dollars (check out our case study here).  

Big finance as incubators and accelerators?

When people hear “fintech,” they often have this image of startups led by hungry 25-year-olds who are burning through Red Bulls and ramen to get through to the next product launch. That may be true to some extent, but the reality is that numerous traditional financial service institutions (think large insurers and banks) are forced by competitive pressures to operate more like fintechs or, surprisingly, have recently started actually incubating or accelerating new companies and brands. 

A good example of this is the major shifts that have been happening in insurance in the last five years. The entry of younger insurance companies such as Lemonade in the US or Sonnet in Canada have forced legacy insurance companies to change how they operate. These new competitors bring features like on-the-spot purchasing and activation, risk-free underwriting, and intuitive and seamless buying experiences for consumers. 

Because of the limitations they have in place due to legacy technologies, it’s sometimes easier for these companies to buy or create new ones from scratch to offer services that are more responsive to customer needs. This is why you see big banks here in Canada like RBC operating a venture studio (RBC Ventures) focused on creating new fintech companies or other players such as Scotiabank buying up more lean competitors like Tangerine, again, to offer more cutting-edge features. While these initiatives are promising, it's still too early to determine if they will be able to fully meet ever evolving customer needs.

Lack of growth-hacking mentality

In the first piece in this series, we’ve touched upon the fact that often at TDL we are brought into companies that already have a behavioral science unit in order to drive that team to produce actual tangible results. These teams are often composed of academics that might not be accustomed to working in the private sector, which has a few repercussions: 

  1. Rigor: They often do not match the level of rigor required for a business problem. For instance, they might conduct randomized controlled trials (RCTs) when unnecessary, resulting in experiments that take a long time and yield no actionable results.
  2. Speed: They frequently lack the ability to operate in a lean manner by applying the 80/20 rule. This makes them not as business-savvy (they might be fast on an academic timescale but slow on a business one!).
  3. Adaptability: They are often not as adaptable in environments with many limitations. To succeed, team members need to find practical solutions that may not be the most ideal. This “MacGyver” mentality is probably the hardest skill to hire for!

To some extent, it’s expected that large, risk-averse organizations would gravitate towards hiring those who hold academic excellence (and hence scientific rigor) in high regard. Fortunately, we have seen a shift in recent years (arriving at the conclusion that we’ve had from the onset here at TDL): companies that previously only hired PhDs for behavioral science implementations are now recognizing that these three key elements—rigor, speed, and adaptability—are also critical in order to drive real ROI. 

Lean Rigor to the rescue?

At TDL we sometimes refer to the people who are not aligned with the above trifecta as missing “Lean Rigor.” You might find the name silly (hey, we coined it!), but it embodies the combination of lean methodology principles (often used in the startup world—see “The Lean Startup”) with rigorous, evidence based decision-making processes that have the right level of evidence for the business objective at hand. Lean Rigor is comprised of five main parts:

  1. Efficiency and Speed: Applying lean principles to streamline processes and reduce unnecessary steps, ensuring that projects move forward quickly without sacrificing quality.
  2. Data-Driven Decisions: Utilizing rigorous data collection and analysis to inform decisions, ensuring that strategies are based on empirical evidence rather than intuition or assumptions.
  3. Continuous Improvement: Implementing a feedback loop where outcomes are constantly measured, evaluated, and improved upon, fostering a culture of perpetual enhancement.
  4. Cross-Disciplinary Integration: Combining insights from various fields such as behavioral science, economics, and data analytics to create well-rounded, effective strategies.
  5. Customer-Centric Focus: Ensuring that all improvements and decisions are made with the end-user in mind, aiming to enhance the overall customer experience.

This approach has served us well across all of our projects and often allowed us to circumvent limitations such as those imposed by legacy systems. Lean Rigor is such an important part of our culture that its influence for us extends beyond just the intersection of finance and technology: part of our hiring process is aimed at testing for all of the above. 

Building a lasting legacy

While we’ve seen how legacy systems and a lack of a growth-hacking mentality continue to create roadblocks, there are also significant strides being made by both traditional financial institutions and innovative startups. 

First, organizations are learning that hiring experts that have significant private sector experience leads to better results. This is also due to the fact that the entire behavioral science community has done a good job of promoting that shift and moving away from putting academics on a pedestal. 

Secondly, organizations such as TDL have come up with nifty frameworks and concepts that can deliver results despite such roadblocks. We’ve talked here about how powerful Lean Rigor can be by offering a dynamic mix of lean methodology and rigorous, data-driven decision-making.

It’s clear that, like many other sectors right now, the financial sector is at a tipping point—it’s just tipping a bit more slowly due to both technological constraints and factors related to organizational culture. That being said, we believe that as more companies adopt these principles, we’re going to see a wave of transformation that will not only meet but exceed user expectations. I can imagine that in a few years from now, we’ll be living in a world where most interaction with your financial apps feels intuitive, personalized, deeply satisfying, and aligned with both your ethical, moral, and financial goals. That’s the future we’re all working towards. 

About the Author

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

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