Eventus’s focal areas so far in 2024 include multi-asset class trade surveillance, and performance improvements.
“More clients are asking about multi-asset class surveillance,” Eventus CEO Travis Schwab told Traders Magazine March 12 at FIA Boca. “In the past few years it’s becoming more and more prevalent.”
Schwab cited new U.S. Securities and Exchange Commission fixed income rules that require some proprietary trading firms to register as broker-dealers, as well as the recent SEC approval of spot bitcoin ETFs, as factors that are increasing demand for multi-asset class trade surveillance.
“Trade surveillance technology that looks for manipulative activity across asset classes is becoming a need,” Schwab said, adding that he expects all trading firms will be using multi-asset class surveillance technology for at least some part of their business within three years.
Regarding performance, Schwab said trade surveillance needs to be better to handle increased volumes in listed derivatives and other markets. Eventus is working on better utilizing cloud to be more flexible and scalable, and also being more efficient in how trade data is moved and managed across ecosystems.
“We’re doing ‘under the covers’ work that helps performance and scalability,” Schwab said.
Schwab said there are many great use cases for AI, and Eventus is working to expand its usage of the emerging technology, for example to enable clients to be more proactive and query the system more directly and with better questions.
However, Schwab emphasized that any AI deployment can’t compromise system performance, and must be fully explainable. “Everything we do has to be explainable,” he said.
The trade surveillance business is becoming more competitive, but some providers have limited offerings, Schwab said. “There are very few firms that can provide enterprise grade surveillance across multiple asset classes and jurisdictions at scale.”
Schwab cited a broad trading industry trend toward more generalized analytics platforms. “The biggest bang for the buck we can provide to clients is a normalized data set. If you then give ChatGPT on top of that with powerful surveillance, you get insights that weren’t readily available before.”
“Where it’s going is, we’ll be able to connect data worlds and have the machine pull context,” Schwab added. “Nobody’s there yet. It’s hard to stitch together data sets. You can have grand visions, but you still have to get the data into the system and it has to be understood.”
