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Three Human-Centric Practices to Become a Better AI Fintech Leader

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1000 Days Out | Ramy Nassar | Jul 8, 2021

3 human centric practices to become a better fintech leader 1 - Three Human-Centric Practices to Become a Better AI Fintech Leader

You’ve seen them before - robo-advisors, virtual agents, smart banking “platforms” - yet with each new product launch and press release, doesn’t it seem that artificially intelligent tools aren’t living up to their self-proclaimed smarts?

It happens with every technology wave, and as far as tech waves go, AI is a tsunami. It goes a little something like this: new technology evolves, executives insist it’s just a fad, their competitors embrace the tech, and then everyone plays catchup. Generally, end-users are the ones who feel the impacts of this cycle, left with an endless barrage of apps & platforms that don’t solve meaningful problems.

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At this point, I may sound quite pessimistic about the potential of AI to create meaningful and measurable value in the FinTech sector. I’m not! In fact, quite the opposite - I believe that machine intelligence has the potential to transform the banking industry - if we take a human-centric approach to designing solutions (while we’re at it, let’s stop creating apps & platforms and all agree to design solutions instead).

There are three practices that FinTech leaders can adopt to bring aspects of a more human-centric design lens into how they develop new AI-enabled solutions.

1 - Understand Data Bias

We all have biases. It’s true. They don’t make you a bad person, in fact, one could argue that it makes you human. It’s not recognizing our own biases that can lead to bad decision making and the same can be said for the data we work with.

Beyond just collecting and analyzing data, organizations need to uncover how data bias can influence the accuracy or reliability of predictive systems. A comprehensive understanding of the role that data bias can play is crucial in building the next generation of intelligent FinTech solutions. As powerful as AI and machine learning tools are, they have the potential to also amplify biases that exist in our data.

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A simple experiment to see how bias can rear its head in an AI-enabled system can be demonstrated with a Google Images search for the term “CEO”. Do the results look like what you’d expect? While at first glance, the results may be surprising, it’s worth noting that as of May, 2020, the Fortune 500 included only 37 female CEOs - a little over 7%.

Data bias can be amplified by algorithms and it’s important to develop a deep understanding of how this risk can be mitigated.

2 - Be Curious!

A person’s IQ is of course their “intelligence quotient” and their EQ their “emotional quotient” - but what about CQ? CQ, or a person’s “curiosity quotient”, has been described by Dr. Tomas Chamorro-Premuzic as “the ultimate tool to produce simple solutions for complex problems.” Given the complexity of working with an emerging technology such as AI, individuals with a high CQ are perfectly positioned to help design clever, impactful solutions.

Curiosity begins with asking the right questions and similarly, human-centered design begins with understanding who you’re designing for and what problem you’re trying to solve. Curiosity is a core competency for leaders wanting to leverage the full potential that AI has to offer in creating scalable FinTech platforms.

3 - Focus on KPIs Over FYIs

AI gives us the remarkable ability to predict future outcomes or find proverbial needles in our data haystacks, but too often organizations focus on data that doesn’t really matter. Most executive dashboards bring together data from across the organization but rarely are these data points actionable and usually they are just FYIs. Interesting insights that give leaders little to no ability to make future decisions from.

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KPIs instead are the data insights that allow leaders to make strategic decisions, in (near) real-time, that positively impact the future of the organization.

 

FYI - For Your Information KPI - Key Performance Indicators
Last quarter or month’s sales performance against target Last quarter or month’s requests for support from internal pre-sales engineers which gives insight into future sales performance
Current warehouse inventory levels across distribution centres Social media data to predict future product demand (or lack thereof)

 

The latter example was made famous by PepsiCo who used social media trend data to forecast changes in consumer behaviour and a decreased demand for sugar-loaded drinks. True KPIs (maybe they should be called Key Performance Levers or KPLs) give business leaders a true insight, based on which resources can be allocated and investments made. When used correctly, AI can be a powerful tool in the FinTech toolbelt for unpacking and making sense of complex data.

Just as the Design Value Index showed that organizations that focus on design outperformed the S&P by 211% over a 10 year period, this mindset can give AI Fintech leaders a similar advantage. The practices outlined above are the starting point for a repeatable framework for designing better AI-enabled FinTech solutions.

About the author:

Ramy is the founder of 1000 Days Out, Head of Design at Olive Group, and author of the upcoming AI Product Design Handbook. As the former Managing Director of Design & Strategy for Architech and Head of Innovation for Mattel, he has led diverse teams in the creation of disruptive new digital products, services & platforms. Ramy teaches Design Thinking at McMaster University and in the Master’s of Engineering, Innovation & Entrepreneurship program at Ryerson University.

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This article appears in NCFA's digital magazine, Fintech Confidential (Issue 3). Click to read the latest thought leadership, insights and trends about Fintech in Canada:

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