How fintech companies and other firms succeed using AI

Fintech firms succeed in using AI by deploying algorithms to deliver products and services in ways that only machines can achieve.
3 October 2023

Algorithms at work: fintech firms are using AI to navigate the complexity of the financial ecosystem.

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Fintech firms stand out from the crowd by bringing bright ideas to the financial services sector. And a big part of those success stories is the adoption of machine learning and AI tools into fintech products. In fact, it’s interesting to note that – according to McKinsey analysis and Bank of England data – 72% of financial services firms have implemented AI in some form, compared with 55% for businesses generally.

Financial services companies are ahead of the curve in other areas such as quantum computing, where qubits have the potential to solve problems beyond the reach of classical processors. Use cases for early adopters include financial risk management and compliance – for example, running Monte Carlo simulations on NISQ devices to examine how much capital to hold for worst-case scenarios.

Navigating a sea of signals

Some might be wondering why fintech companies and forward-thinking financial services providers deploying AI and other solutions have a habit of being at the cutting edge. “It’s pretty simple,” Ronald Binkofski – CEO of STX Next, a global IT consultancy company – told TechHQ. “Financial transactions can be very complex.”

Binkofski draws attention to the scale of the ecosystem and the frequency at which information is sent and received. The situation makes it impossible for humans to stay on top of things without help from algorithms and automation, and it speaks to fintech firms being progressive in their adoption of AI.

For example, traders need to be able to extract information from a sea of signals and prioritize their response. In e-commerce, systems should ensure that customers receive the products and services they are expecting and don’t get cheated. Banks need to differentiate between genuine clients and bad actors.

“It’s not perfect, but in a short period of time, the technology will manage it,” comments Binkofski, noting how systems such as voice recognition can go beyond the frequencies that people can hear to bolster defenses against fraud.

Global IT firms such as STX Next and others play a key role in turning the ideas of fintech companies and other organizations into reality. Binkofski and his team have supported numerous projects, from an industrial IoT perfume-making machine to an interactive web experience exploring early Buddhist texts.

Zooming into fintech, AI solutions can help on multiple fronts. Data analytics opens the door to more personalized services and understanding what customers want based on large volumes of information. AI, machine learning, and automation make solutions scalable, so that having more customers doesn’t stretch resources too thin.

As we’ve written about on TechHQ, financial services providers have been quick to explore the application of AI techniques in combatting fraud, using audio-based authentication to improve customer experience. Advances in the use of biometrics mean that security doesn’t have to be as onerous for clients who may be anxious to access help.

There are numerous benefits that fintech firms and other companies can access through AI-powered solutions. But success isn’t just about having the right software development partner. Project management skills are vital too, and STX Next has in-house teams to keep everything on track.

Fintech AI roundtable

Tech team: Ronald Binkofski (center) and colleagues put a human perspective on what it takes for fintech and other partners to realize AI-enabled products and services. Image credit: STX Next.

Returning to Binkofski’s comments on the complexity of the financial ecosystem, banks started building out their mainframe systems in the 1970s, and those designs aren’t easy to change. “They can be a closed black box with a few touch points,” he explains.

In such cases, accessing data is more likely to involve building bridges rather than starting over, particularly if the original authors of the software are no longer around. There can be a lot of abstraction to deal with in coupling legacy systems with state-of-the-art solutions, but the outcomes make the challenges worthwhile.

“Be surprised by the future and be happy about how people will drive the technology,” Binkofski advises company leaders who are feeling overwhelmed. “If the positives outweigh the negatives, then we are on the right lines.”

Rather than seeing AI as an existential threat, he’s on the side of algorithms helping to navigate what would otherwise be an overwhelming amount of information. Machines are ideal for handling other machines and handing over insights in a way that humans can understand and act upon.

Standing still is not a viable strategy and could be another reason why fintech firms are open to new ideas, such as the use of AI and pursuing other novel approaches. Blockchain solutions are challenging traditional ways of doing things; the financial services sector as a whole is a curious mix of firms at the bleeding edge and companies in need of help to catch up.

Another puzzle is how to bring order to the virtual world. “Nothing is physical, which makes it hard to regulate using methods in the physical world,” Binkofski believes.

It’s possible that we may need a new legal system dedicated to the virtual world, and solutions could be required sooner rather than later as enterprises embrace the opportunities of hardware such as VR headsets. Extended reality (XR) includes spatial computing and mixed reality, which blend real and virtual environments, further complicating the topic of regulation.

Fintech AI playbook

One of the takeaways from fintech’s AI success stories is that the firms in the lead are the ones embracing the opportunities. There are failures, but it’s unrealistic to expect perfection from day one. In financial services and elsewhere, the use of machine learning algorithms to make sense of data is transforming the delivery of services to clients.

Also, AI often adds value to what’s already there. For example, in brick-and-mortar retail, machine learning can build personas out of what’s in the shopping basket helping store owners to run their operations more efficiently.

With the right partners, firms can boost their performance, and the process begins with a conversation. STX Next runs discovery workshops – which feature a product consultant, product design lead, and software architect – to help firms set out the best approach for realizing business ideas.

And the approach is a reminder that reaching out and understanding the technology options and being able to validate that products match user expectations is important. AI is the bright light, but there’s lots more that makes companies successful in fintech and elsewhere.