OpinionBetting the bank on GenAI, is it worth it?
Opinion
Betting the bank on GenAI, is it worth it?
Navigating opportunities and limitations on generative AI within financial services - without the hype
Picture this: Wall Street trading floors dominated by AI bots, executing billions of trades per second, optimizing portfolios with an intelligence that dwarfs human capabilities. A world where GenAI-powered hedge funds relentlessly analyze global markets and execute trades before a human trader can even blink. Sounds exhilarating — or terrifying — right? Let’s push pause on this creative vision and dive into our current reality, breaking down what Generative AI (GenAI) can actually do in the financial services sector.
In the last couple of weeks, I’ve had insightful conversations with an array of fintech startups and financial industry veterans. My goal is to separate the sensational from the practical when it comes to GenAI.
Understanding the realm of GenAI
First things first, let’s get acquainted with GenAI. It’s a subset of deep learning, focused on generating content be it text, images, code, or videos. Deep learning, a behemoth in its own right, has a broad spectrum including predictions, classifications, and clustering. GenAI, however, is like a specialized surgeon wielding a scalpel – it excels in generating new content, especially when dealing with unstructured data.
Riding the hyper-personalization wave
One of the most promising applications of GenAI in financial services is personalized customer interactions. By analyzing data on customers’ preferences and behavior, financial institutions can use GenAI to create tailored marketing, sales strategies, and customer support. Think of it as crafting detailed and customized financial advice or product recommendations based on each customer’s unique financial history and aspirations.
Core processes: Tread with caution
Core banking and insurance processes, such as underwriting predominantly handling structured,transaction-based data, are areas where GenAI’s application is inherently limited. Furthermore, the lack of explainability in GenAI models poses a challenge in industries governed by stringent compliance and regulatory norms, which often demand transparent AI decision-making processes.
The text-heavy bonanza
The mid-term gold lies in text-heavy process automation. GenAI has a lot of potential to revamp areas such as claim management, collection practices, and reconciliation automation. Handling unstructured data is GenAI’s bread and butter, making these segments ripe for the picking for investors looking for solid, scalable investment targets.
BPO disruption: The double-edged sword
The business process outsourcing (BPO) sector, encompassing call centers and IT services, is ripe for disruption. GenAI’s capabilities can drive automation to enhance efficiencies. However, this sector may also face turbulence as GenAI-native entrants challenge the status quo.
Navigating regulatory waters
Regulatory compliance is a significant factor in financial services. Investment in GenAI-driven solutions must take into account the evolving regulatory landscape. Backing ventures with a clear understanding of regulatory constraints and a roadmap for compliance is prudent.
The road ahead
As we stand on the fresh stage of GenAI's integration into financial services, this technology is set to mature and potentially find broader applications and transformations. Stakeholders in financial services should closely monitor these developments to strategically position themselves in this dynamic landscape.
In conclusion, while still emerging, GenAI’s ability to revolutionize consumer interactions through hyper-personalization and its expertise in automating text-heavy processes, marks it as a game-changing technology within the broader spectrum of deep learning applications in financial services.
Maya Hawlasewicz is a Principal at Pitango Growth
