Trading Reimagined is a content series that examines how the transformative power of technology is prompting a reimagining of the markets. Trading Reimagined is sponsored by Exegy.
Electronic trading systems were developed as far back as the 1970s, but they were mostly the realm of the largest, deepest-pocketed financial firms until the Internet revolution of late 1990s.
Even after smaller and mid-sized brokers adopted electronic trading, a substantial bifurcation in capabilities endured. Only in recent years has this gap narrowed, as innovation, cost efficiencies and democratization efforts by third-party vendors made advanced tools and technologies more broadly available to mid-sized and smaller firms.
A similar setup may be developing in the latest technology that’s expected to have transformative effects on the industry: artificial intelligence. At least with regard to how AI will impact operational risk, cybersecurity, and fraud challenges.
In a March 27 report, the U.S. Department of the Treasury identified opportunities and challenges that AI presents to the security and resiliency of the financial services sector. The top two next steps suggested by the Treasury speak to the emerging large firm / small firm divide.
“There is a widening gap between large and small financial institutions when it comes to in-house AI systems,” the Treasury report noted. “Large institutions are developing their own AI systems, while smaller institutions may be unable to do so because they lack the internal data resources required to train large models.”
The Treasury highlighted data as a critical component of an effective AI system – and potentially a key structural disadvantage for smaller and mid-sized firms.
“As more firms deploy AI, a gap exists in the data available to financial institutions for training models. This gap is significant in the area of fraud prevention, where there is insufficient data sharing among firms,” the report stated. “As financial institutions work with their internal data to develop these models, large institutions hold a significant advantage because they have far more historical data. Smaller institutions generally lack sufficient internal data and expertise to build their own anti-fraud AI models.”
Electronic trading didn’t proliferate with all trading and investing firms developing their own in-house systems, rather it was more a function of the technology becoming feasible and cost-effective for market participants to acquire via solutions providers, and then collaborate with the vendor to optimize.
The Treasury report suggested the AI race will play out similarly, with extra importance attached to choosing the right technology partner.
“The importance of data for AI technology and the complexity of AI technology development would very likely increase financial institutions’ reliance on third-party providers of data and technology,” the report stated. “As a result, it is very likely that often overlooked third-party risk considerations such as data integrity and data provenance will emerge as significant concerns for third-party risk management.”
