When ChatGPT debuted on November 30, 2022, it created a seismic shift in the public’s expectations of what a generative AI could do. The AI chatbot reached one million users in just five days, and by January, it had become the fastest-growing platform in history with over 100 million users.
People who had never considered using AI could now easily access a user-friendly AI platform and experiment with how the technology could save them time or enhance their work. Fast-forward a few months, and every industry is now exploring its workflow applications, including financial services.
Naturally, the technology has created some fear and trepidation along with a fair dose of optimism. Industry leaders are wondering: Can we utilize this technology to build exciting new solutions … or will we be out of business in five years?
Creating a Dialogue about the Impact of Generative AI on Asset Management Operations
In my 20+ years of asset management and fund administration experience, I’ve seen plenty of changes, But honestly, generative AI has created the biggest reaction and the most uncertainty. My role at Linedata affords me the opportunity to talk with C-level executives at some of the biggest names in private credit, private equity, and hedge funds and hear the challenges they’re facing.
4 Observations About AI and Financial Services
1. Curiosity Over Application
To paraphrase Raoul Pal, CEO and Co-founder of Real Vision Group, we are entering the “Exponential Age” of life and technology. While Artificial Intelligence was invented almost 50 years ago, the introduction of Generative AI (GAI) has democratized the technology and ignited the populace’s imagination.
That being said, in our industry, I’m currently seeing more AI curiosity than implementation. It’s a trust, but verify situation. In financial services, firms are either releasing AI solutions only in beta, or they’re utilizing the technology in a very limited capacity.
Here’s the reality — firms don’t really understand GAI yet. So while generative AI is a captivating technology in terms of its possible capabilities, there’s still so much to learn about how it can enhance current solutions.
This apprehension with an emerging technology is nothing new. Adoption doesn’t happen overnight. Rather, it’s a series of trials and errors. When I first entered the industry, trades still involved runners taking trades from brokers to the trading floor. Now almost all trades are conducted online, but that was a long road that took 15-20 years. Generative AI will likely follow the same iterative trajectory, just over an accelerated timeline. So while all financial services companies are looking into AI, the actual applications are sparse.
2. Firms Will Adopt AI Before They’re Comfortable With It
As a society, we’re much more comfortable with adopting new technology than we were 30 years ago. As such, users are less scared of applying new tech to their workflows. That comfortability with technology paired with the highly competitive nature of our industry will fuel an accelerated rate of adoption for AI.
Industry leaders rightfully see AI as a potential game changer, so they will naturally seek any competitive advantage that AI can provide. All it will take is a few early adopters to swing the pendulum and encourage more firms to deploy AI applications.
The fear of being left behind is real. So even if the thought of turning over operational workflows to an AI-powered application makes some leaders queasy, they will do it before they feel completely comfortable.
3. Early Adopters Will Take on Considerable Risk
Most of the AI chatter I’m hearing right now is talking about all of the possibilities this new technology brings. But what a lot of the dialogue is missing is an honest discussion of the (very real) risks.
-What happens if firms adopt an AI application before it’s been thoroughly tested and vetted against all potential exposure points? All of the sudden, firms could be exposing their data to a whole host of cybercriminals who would love access to this previously secure information.
And it’s not just potential exposure to nefarious actors that firms should be worried about. What happens if your firm’s proprietary data (like positions or statistics) becomes part of a third-party platform’s large-language model? Now other firms will be able to utilize your firm’s proprietary models or hard-earned data to enhance their investment thesis or client offerings.
Additionally, we operate in a highly regulated industry, and many of the proposed AI use cases provide an uncertain (at best) regulatory compliance position.
In the end, there are numerous risks involving compliance, PPI (private personal information), proprietary data, and more.
4. Everything is Going to Change, and Quickly
As eager as we are to learn more about this new technology, it’s important to remember that this is also just a single point in time. As AI evolves, perspectives on its applications will change rapidly. Even as soon as six months from now, we may be having a drastically different conversation.
Because the AI landscape will change so quickly, it’s important to have a continuous discussion. This piece is designed to serve as the icebreaker. In future posts, we’ll add insights from all areas of the industry: C-level executives, regulators, data scientists, and even vendors.
This is the start of a journey, so stick with us as we dive into generative AI for financial services in the following months.
About the author, Jonathan Hinkley
Jonathan Hinkley, is the SVP, Middle and Back Office Services at Linedata. He is a financial services leader with 20+ years’ experience in Investment Management Middle/Back Office operations and Public Accounting. Jonathan leads the Middle and Back Office Co-Sourcing business for Linedata Global Services in New York, where he leads multiple cross-functional teams enabling multi-strategy, credit, and private equity firms to maximize operating leverage. Jonathan is a trusted advisor and valuable resource to CFO and COOs, with an extensive background in creating scale servicing funds across the capital structure with a particular focus on complex Credit Funds. Jonathan holds a Bachelor of Science in Accountancy from Bentley University and has held a Certified Public Accountant license (MA) since 2001.
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