GenAI: Bringing endless possibilities to the fintech sector

Finance firms have little option but to invest in new and exciting Generative AI capabilities, or risk being left behind by their closest competitors

It’s pretty much impossible at the moment to discuss fintech without referencing Generative AI. 

Last year saw the worlds of money and technology coming together in a host of new ways, and those innovation levels are only set to increase throughout 2024 as companies clamour to increase productivity levels and get a leg-up on their competitors. 

There has long been awareness of the potential of GenAI, but its influence across the fintech space and technology, in general, has grown at a pace that few could have foreseen. 

“We have been close to Gen AI developments in recent years, so we weren’t surprised by its capabilities,” says Richard Berkley, Head of Data, Analytics and AI in Financial Services at PA Consulting.

However, referencing the astonishingly popular launch of ChatGPT, he adds: “What has surprised me is the speed and scale of market activity it generated – with 100 million active users within two months and every board member and executive wanting to know what it means for their business.”

Claudio Truzzi, Director of the Research and Innovation Activities Support Office at the Université Libre de Bruxelles in Belgium, calls the rapid rise of Gen AI in fintech “remarkable”.

He continues: “At first, AI in finance was mostly used for number crunching – analysing data, building models. But Gen AI can do so much more, changing how we think about, deliver and run financial services. 

“Unlike specialised AI, which typically follows predefined rules and models, Gen AI's unique strength lies in its ability to create and innovate.”

Possibilities endless thanks to GenAI

Almost all organisations operating in the financial services sector, from established large banks to fintech scale-ups, are prioritising how to become more agile, scalable and future-ready.

Erin Nicholson, Global Head of Data Protection and Privacy at Thoughtworks, highlights the proliferation and commonality of digital payment methods – like Apple Pay – as a good example of how this has already happened in recent years. 

Now, “data-savvy players” are using AI to lower costs and deliver even more innovative offerings. 

“With more data, Gen AI can be used to create personalised payment experiences, such as suggesting relevant payment methods or status updates, aid in fraud detection, and to help improve customer loyalty and satisfaction,” Nicholson says. 

In fact, the possibilities are seemingly endless when it comes to the ways financial institutions can use AI to enhance their services.

Eric Lefebvre, CTO at Sovos, references AI’s ability to detect patterns and anomalies – critical when it comes to cybersecurity, securing transactions and authorising users.

“There are a number of other areas in which AI can study patterns of customer interaction and system usage to help better plan for everything from system capabilities and uptime to customer service, monetary flows and staffing,” Lefebvre adds. 

While Gen AI is completely changing the face of some industries, fintech has always been a data-centric sector in terms of the volume of transactions and the significant benefits that can be derived from spotting patterns in data to predict future trends.

It’s for this reason that Dom Couldwell, Head of Field Engineering, EMEA at DataStax, believes real impact in the sector is yet to materialise.

“I think the influence will grow once organisations are more willing to ‘take the training wheels off’,” he says. “It’s understandable that, in a highly regulated industry, organisations will be cautious about how much control they give away.”

Combatting fraud

It goes almost without saying that fraudsters are already utilising the capabilities of Gen AI to target both companies and consumers with increasingly sophisticated scams, from fraudulent data generation to social engineering and phishing. 

“Our experts have observed several emerging economic crime risks within Gen AI,” reveals Berkley. 

“Financial institutions and fintechs need to understand that the misuse of Gen AI to perpetrate fraud is becoming an increasing risk to both them and their customers.”

However, on the flip side, this game-changing technology is providing organisations with more intelligent tools to assist them in detecting and combatting fraud.

What’s perhaps most exciting is that, soon, enterprises will be in a position where their approach to fraud prevention is far more proactive than reactive, thanks to Gen AI’s ability to continuously learn and evolve. 

“Generative AI can play a critical role in enhancing fraud detection and risk management capabilities within the fintech industry,” Couldwell adds.

“AI models can analyse real-time transaction data, identify patterns and anomalies, and flag suspicious activities in a timely manner. This proactive approach can help financial institutions prevent losses, protect customer data and maintain regulatory compliance.”

Clearly, financial institutions have been employing technology designed to detect abnormal user behaviour for decades, but now the ante has been upped significantly thanks to the greater precision made possible by Gen AI. 

“AI’s ability to study user behaviour and detect patterns can create a strong user profile,” Lefebvre explains. “So, when transactions are initiated that fall outside a customer’s typical user pattern, it can be flagged and escalated properly, preventing fraud and eliminating potential losses.”

Keeping up with competitors

Finance firms seemingly have little option but to bet big on Generative AI, or, at the very least, carefully plan their future investments. 

What looks even more certain is that hesitation must surely be avoided at all costs, notwithstanding legitimate concerns over implementation, security, compliance and more. 

“It’s vital to create well-functioning digital infrastructure – sustainable and safe AI depends on it,” adds privacy expert Nicholson. 

“To ensure any models we train ourselves are accurate and fair, we also need to keep an eye on data quality. Not all data was created equally and, if the data is inaccurate, biased or outdated, this will impact the model it is training.”

However, asserting the importance of committing to Gen AI, Brian Halpin, SVP Internal Automation at SS&C Blue Prism, is unequivocal.

“Businesses in today’s economic climate need to be able to keep up with their competitors, and they need to continue to become even more advanced than their adversaries in the capabilities that are out there,” he concludes. 

“It’s not an option for companies to invest slowly in Generative AI capabilities. Given how accessible these tools are through open source and various low-cost commercial models, it is vital for companies to stay ahead of the curve, or risk falling too far behind.”


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