Generative AI in Fintech: Unleashing Potential Beyond ChatBots
Fintech’s AI journey has been nothing short of remarkable. The fintech industry, long accustomed to artificial intelligence (AI), is poised for even greater strides, as highlighted by Isabelle Castro Margaroli in Fintech News. According to projections, the AI in fintech market is set to reach a staggering $31.71 billion by 2027, exhibiting a remarkable growth rate of 28.6%. The utilization of AI in fintech has already found its footing, with a staggering 90% of fintech companies already incorporating AI into their operations, as reported by the Cambridge Centre for Alternative Finance.
Yet, within this landscape of technological evolution, a new player has emerged – Generative AI. This novel technology promises to redefine the way we approach financial services, transcending the conventional ChatBots and automation tools that we have become accustomed to. However, it is important to recognize that the industry has only begun to tap into the potential of Generative AI, and there is a considerable journey ahead before it reaches its zenith.
Robert Antoniades, Co-Founder and General Partner of Information Venture Partners, aptly encapsulates the current state of Generative AI in the financial sector: «How is Gen AI being used by financial services? The simple answer is it’s not being used. Certainly not broadly. But what Gen AI has done is it has increased the recognition of the power of AI for financial institutions.» Antoniades elucidates that while companies have started deploying Generative AI tools like Chat GPT to streamline customer-facing processes, the technology’s true transformative impact could lie in revolutionizing the backend of financial services.
However, a significant hurdle remains – accuracy. For Generative AI to fulfill its potential in financial services, it must achieve absolute precision. Regrettably, this precision is currently beyond reach. Recent events have highlighted the perils of inaccurate Generative AI. On June 1, an upheaval ensued on social media when «anonymous sources» seemingly reported that SEC Chairman Gary Gensler had resigned pending an «internal investigation.» Subsequently, these claims were exposed as false, and the source of these erroneous reports? A Generative AI bot.
Antoniades emphasizes the critical need for absolute accuracy in financial services, stating, «You have to understand that in financial services if it’s anything important, it has to be 100% accurate. There’s no room for hallucinations. There’s no room for errors. AI-generated answers are fascinating to see because they’re actually decent, but they are not accurate.» The ramifications of inaccuracies in financial advice and record-keeping could be catastrophic.
However, the potential applications of Generative AI in financial services are profound. Financial advisory services, often out of reach for many due to cost constraints, could become more accessible through Generative AI. This technology could customize advisory services based on individual customer interactions, providing tailored guidance and support.
Antoniades underscores this point: «Gen AI is actually a very interesting use case there of how to provide that interaction and contextualization between the customer and the financial institution. By ingesting all that data, it can now have what one would consider a conversation with a client.»
Moreover, Generative AI has the potential to significantly enhance fraud detection and anti-money laundering (AML) efforts, building upon the already increasing reliance on AI and machine learning models in these domains.
One particularly disruptive application of Generative AI is modernizing the aging infrastructure of the banking system. Powered by COBOL, a programming language dating back to 1959, this antiquated framework has remained largely unchanged while technology has raced ahead. This outdated system is cumbersome to adapt and requires extensive custom programming for any updates.
Antoniades proposes a solution: «I think about this as a way to modernize infrastructure. Generative AI could be used to rewrite the archaic COBOL code and provide a patch to expedite the transition to a new, modern infrastructure.» The stakes are high when dealing with legacy systems, as any errors could have catastrophic consequences.
As Antoniades succinctly puts it, «When you make a deposit in your bank account, you want to know the money’s there. It’s not that it can be there 99.9% of the time. It’s always there. When they give you advice, they really should be 100% accurate. It shouldn’t be 90% accurate.»
The potential for Generative AI in fintech is undeniable, and financial institutions are acutely aware of it. The industry’s next challenge is to push forward with development until GenAI’s outcomes approach perfection. The horizon of financial services is poised for transformation, and Generative AI is the harbinger of this profound change.