Major banks and fintech companies are diving headfirst into generative artificial intelligence as the hype surrounding this cutting-edge technology continues to build. While the potential for innovation appears limitless, concerns about potential pitfalls and risks persist.
At the Money 20/20 fintech conference in Amsterdam, executives from prominent lenders and online finance firms expressed their enthusiasm for generative AI, heralding it as an “explosion of innovation” that will unlock new possibilities beyond our current imagination.
Chalapathy Neti, the head of AI at global bank messaging network Swift, marveled at the progress made with ChatGPT and GPT-4, calling it “mind-boggling” and emphasizing that we are truly witnessing a transformative moment.
However, banks are grappling with the challenge of identifying the most effective use cases for this technology in the short term. One such example is ABN Amro, a banking giant based in the Netherlands, which is currently piloting the use of generative AI in its operations.
Annerie Vreugdenhil, Chief Commercial Officer of ABN Amro’s personal and business banking division, revealed during a panel discussion that the bank is leveraging generative AI to automatically summarize conversations between bank staff and customers. It also aids employees in gathering customer data to improve query responses and minimize repetitive questions.
ABN Amro is now in the process of scaling these pilots to involve 200 employees, while also exploring new pilot projects scheduled to commence this summer.
In a closed-door session focusing on the application of AI in financial services, two banking executives shared insights into how they are utilizing generative AI to enhance their internal code and analyze client behavior.
Mariana Gomez de la Villa, an executive at ING Bank specializing in strategy and innovation, emphasized that they are in the experimental stage and are not yet implementing client-facing solutions. However, they are leveraging the technology for tasks such as code refactoring and communication analysis.
Interestingly, the banks present at Money 20/20 seemed to unanimously exercise caution when it came to deploying ChatGPT-like tools in customer-facing scenarios.
One crucial concern lies in the significant volume of data required for advanced AI systems, a sensitive commodity regulated by various rules and guidelines. Jon Ander Beracoechea Alava, the Head of Advanced Analytics Discipline at Spanish bank BBVA, described their approach as “conservative.” He noted that generative AI is still in its early and immature stages, making it too risky to involve sensitive customer information at this point.
Generative AI, explained:
Generative AI refers to a specific form of AI that has the ability to produce content from scratch. These systems take user inputs and utilize powerful algorithms driven by extensive datasets to generate new text, images, and videos that closely resemble human creations.
The technology gained significant attention following the success of OpenAI’s GPT language processing technology. ChatGPT, which employs massive language models to generate human-like responses to questions, has sparked an arms race among companies vying to harness the next paradigm shift in technology.
In March, Goldman Sachs’ Chief Information Officer, Marco Argenti, revealed that the bank is internally experimenting with generative AI tools to assist developers in automatically generating and testing code.
More recently, in May, Goldman launched its first startup from the bank’s internal incubator: Louisa, an AI-powered social media company tailored for corporate use. This foray into AI aligns with CEO David Solomon’s broader efforts to accelerate the bank’s digital transformation.
Morgan Stanley has also embraced generative AI to support its financial advisors in handling customer inquiries. The bank has been conducting trials with an OpenAI-powered chatbot involving 300 advisors thus far. The ultimate goal is to empower their approximately 16,000 advisors with access to Morgan Stanley’s vast research and data repository, according to Jeff McMillan, the head of analytics and data at the firm’s wealth management division.
A.I. as a ‘co-pilot’:
These examples highlight how financial institutions are leveraging AI as a digital assistant rather than a central component of their services.
Gudmundur Kristjansson, CEO and co-founder of Icelandic regulatory technology firm Lucinity, demonstrated how artificial intelligence can support a critical aspect of finance: combating illicit activities.
Lucinity developed an AI tool called Luci, which assists compliance professionals in their investigations. During a live demonstration, Kristjansson showcased how the AI tool analyzed a money laundering case, providing an independent review based on its analysis.
In this context, AI acts as more of a resource, or a “co-pilot,” aiding employees in data retrieval and case development instead of replacing the role of a human investigator handling reports of suspicious activities.
Kristjansson explained that money laundering typically involves intricate networks of individuals specifically employed for such illicit purposes, making it challenging to detect. Banks spent a staggering $274 billion on prevention measures this year. Luci significantly reduces the time spent discerning fraudulent or money laundering activities, helping institutions combat financial crimes more effectively.
According to attendees at Money 20/20, the allure of AI for major banks and fintech companies lies in its potential to drastically reduce the time and costs associated with tasks that can take human employees days to complete.
Niklas Guske, Chief Operating Officer at Taktile, a startup focused on automating decision-making for fintech companies, acknowledged the challenges of utilizing AI in the financial sector due to the limited availability of public data. However, he emphasized that AI could be a crucial tool in reducing operational expenses and enhancing efficiency.
Guske highlighted how automation, particularly in customer onboarding and underwriting processes, facilitated by access to extensive data sources, empowers lenders to gain new insights and identify suitable customers without manually sifting through countless PDF documents.
As banks and fintech companies continue to navigate the realm of AI, finding the right balance between innovation and risk mitigation will be paramount in unlocking its full potential in the financial sector.
