In the financial services industry, generative artificial intelligence (AI) is poised to usher in a myriad of opportunities, promising enhanced job productivity, improved efficiencies and hyper-personalization. But despite these prospects, the technology is also expected to introduce a number of risks and challenges which necessitate cross-industry cooperation, experts said during a recent webinar hosted by London-based identity verification specialist Sumsub.

The webinar, titled “Generative AI in Fintech: How It is Changing the Game in MENA” and hosted on February 20, featured top executives from banking group Citigroup, venture investment firm 237 Ventures, and Sumsub. These industry experts came together to shed light on the transformative impact of gen AI in the financial services industry, providing insights into the current state of gen AI adoption, sharing practical applications and exploring future trends of gen AI in the finance and banking sector.

The state of gen AI in financial services

Ronit Ghose, global head of Citi’s Future of Finance, a think tank focused on the future of tech, money and finance, initiated the conversation by noting that while AI has been a stable in banking operations, supporting banks in modeling and risk analysis, gen AI remains largely a novelty. The technology is however being experimented with actively within the financial services industry, with hopes of enhanced productivity across functions such as code generation, documentation and information retrieval.

“Gen AI is relatively new and in most cases, it’s just being deployed,” Ghose said during the webinar.

“Where we see things changing rapidly in financial services in general is the ability to create content. So gen AI is a huge productivity boost for code generation, code audit, documentation, but also in the information search analysis retrieval game where all big information companies, whether explicitly such as consulting firms, or a bank … [These companies] have … huge amounts of information and the ability to extract that information is very valuable.”

Akshay Chopra, managing partner at 237 Ventures and a board member of the MENA Fintech Association, identified Microsoft as a frontrunner in gen AI implementation, citing the firm’s strategic partnership with ChatGPT developer OpenAI. He highlighted Microsoft’s integration of gen AI into consumer and enterprise solutions, such as its productivity software suite Microsoft 365 and search engine Bing, as pivotal advancements in the industry.

“There is a clear leader right now, both at the consumer and also the institutional level: it’s Microsoft, without a doubt,” Chopra said.

“It’s ridiculously impressive how a company that size with stakes as big as theirs has already implemented gen AI in so many of their products. So if you’re a business customer of Microsoft, if you have Microsoft 365, for example, chances are you can already use Copilot, meaning that in every email you write, in every presentation you create, gen AI can already do the work for you.

“I did not see that happening in 2023 when it did, I thought it would come a few years later. Microsoft has already entered the enterprise market with direct-to-employ products, and they already have a big stake in the leader in the space, which is OpenAI.”

Fraud prevention, hyper-personalization among biggest opportunities

Pavel Goldman-Kalaydin, head of artificial intelligence and machine learning (AI/ML) at Sumsub, underscored the transformative potential of AI in enhancing job productivity and efficiency across various sectors, particularly in financial services.

Chopra outlined key use cases for AI implementation in financial institutions, including improved customer service, fraud detection, and hyper-personalized product development. He highlighted success stories such as JP Morgan’s use of gen AI for legal document review, which has led to significant time savings and operational efficiencies, as well as the use of AI by payment networks such as Mastercard and Visa to combat fraud.

He also emphasized the potential of gen AI to create hyper-personalized products. By understanding each customer’s unique preferences, requirements and behavior patterns, businesses can offer tailored products and suggestions, increasing the likelihood of conversion and customer satisfaction.

“Gen AI, with the tremendous capabilities it has and the sheer amount of data it can process, will help build financial products for a segment of [just] one [individual],” Chopra said. “It can create hyper-personalized products which are very profitable for the institution but also very useful for the customer. That’s the biggest change in product management in many industries, especially in financial services.”

Chopra also highlighted the potential of gen AI in other sectors, especially in e-commerce where the technology is poised to transform the shopping experience.

“The shopping experience is going to be entirely different when gen AI is in full maturity,” Chopra said. “If you look at online shopping, we go look around and browse, and it’s a very long discovery process. Gen AI assistants can help you shop very quickly and get exactly what you need very rapidly. That’s something I’m really bullish about.”

New risks to tackle

Goldman-Kalaydin warned about the challenges associated with gen AI implementation in financial services. Drawing from the example of Air Canada, which was recently held liable by the court for its chatbot giving a passenger bad advice, Goldman-Kalaydin cautioned that while gen AI can offer enhanced efficiency and convenience, the technology also poses significant risks and implications for companies. This calls for robust security measures and appropriate safeguarding against rising threats such as deep fakes.

“Any fintech company, every day, gets a deep fake and forged documents,” Goldman-Kalaydin said. “If you really plan to invest in the technology, you not only have to think about how you want to use it to your advantage, but you also have to think about how other people will not be able to use this technology … against you.”

Goldman-Kalaydin also cautioned about the risks of inadequate regulatory preparedness, emphasizing the need for collaboration with legal departments to ensure compliance with evolving AI regulations.

“The biggest risk is not being prepared for regulations,” Goldman-Kalaydin said. “Once you are actually obliged to be compliant, it will take way more resources to actually be prepared for regulation, and this is a very big risk.”

Ghose highlighted the need for a collaborative approach to combat evolving threats and risks posed by AI, and emphasized the need to invest in talent and redesign processes to leverage the full potential of gen AI.

“All of us as institutions will have to work in collaboration with each other and with startups in particular,” Ghose said.

“A lot of the time, the best R&D is happening at small startups and we have to be open to collaboration, especially in this space where you’ll have a group of very sophisticated AI specialists helping us capture fakes and deep fakes. We have to work with a lot of different sources of talent and a lot of them are external, so whether we invest in them and portfolio companies, or as ventures and partnership arms.”

Ghose also noted that while large financial institutions possess significant advantages in terms of financial and data resources, these organizations however face significant regulatory scrutiny, posing considerable challenges to banks seeking to adopt new technologies such as gen AI.

“In some ways, AI is perfect for [the banking] industry,” Ghose said.

“We have huge amounts of data … and the bigger the company you are, the more data you have to exploit, and the more of a budget you have, because some of that stuff is quite expensive to do. The biggest banks in each country will tend to have an edge over their peers just because of budgets.

“That’s said, there is a disadvantage in size and scale as well … and regulatory oversight. Domestically significant financial institutions tend to get much more scrutiny from the regulator.”

On the other hand, smaller banks, fintech startups, and non-bank financial companies tend to benefit from less regulatory scrutiny, Ghose said, positioning them more favorably for innovation and adoption of new AI-driven models.

“If you’re a smaller bank, or a fintech, or a non-bank financial company with the right management and the right skill sets, you’re probably in a good position to benefit because you’re going to have less scrutiny in rolling out these new models.

“Also, if you’re in a jurisdiction, say Singapore or potentially the United Arab Emirates (UAE), where the regulators have a rulebook on this topic, you’re in advantage. If you’re in a jurisdiction with no rulebook or with a rulebook being shaped, so to speak, and you’re a big company, you’re probably at a disadvantage.”

The webinar replay is available on-demand. To watch the replay, visit this link.

Featured image credit: Edited from freepik