AiThority Interview with Jim Kwiatkowski, CEO at LTX, a Broadridge Company

Hi Jim, welcome to our Interview Series Please tell us a little bit about your journey and what inspired you to start at LTX.

I have spent about 25 years in the electronic trading space. Before joining LTX, most recently I was Global Head of Transaction Sales at the London Stock Exchange where I led a global team responsible for FXall and Matching, the leading foreign exchange e-trading platforms for dealer-to-customer and interdealer trading respectively. During that time, I oversaw significant growth through a period in which the majority of institutional FX trading moved to e-platforms. Prior to FXall, I lead sales and client services for an equity trading platform.

My experiences growing institutional electronic trading businesses across asset classes, including listed securities and OTC FX enable me to see how electronic trading benefits customers. I was inspired to leverage those experiences in other asset classes at LTX as the US credit market continues to evolve.

What is LTX and what are your core offerings? How does it make trading smarter and faster?

LTX is a credit analytics and e-trading platform that is majority owned by Broadridge. We’ve combined patented artificial intelligence with next-generation execution capabilities to improve liquidity and efficiency in the credit market.

While approximately 40% of the US corporate bond market (investment grade) is now trading electronically it continues to significantly lag other asset classes that see 80-90% trading electronically.

Our mission is to use data to enable customers to discover trading opportunities more easily and further electronify the market. In keeping with our tagline “Trade Smarter,” on our platform we provide transparency and information that our clients need to help them make informed trading decisions and trade more efficiently.

Our Liquidity Cloud is an anonymous repository of real-time indications of interest that provides unique pre-trade information. A neural network identifies Cloud Matches, indications of natural contra liquidity for a given bond. Cloud Match Scores measure the strength of the match. For example, a Cloud Match Score of 9 means there is a Cloud Match for your bond and 90% of comparable bonds recently traded at the same bid/ask spread or wider (only 10% traded at a tighter bid/ask spread).

In addition to providing unique pre-trade information, our core offering includes our trade execution protocols which facilitate block trading and price improvement.

Recommended: AiThority Interview with James Rubin, Product Manager at Google

CIOs are challenged and overwhelmed by the SaaS sprawl in their finance departments. Could you tell how LTX empowers CIOs in optimizing their trading management with AI and digital tools?

We’ve partnered with 6 leading order management system (OMS) providers to make LTX pre-trade capabilities available directly within our clients’ daily workflows.

These capabilities include AI and data-powered tools that allow investors and traders to better inform their decision-making and optimize trade participation. In particular, our AI-driven insights around pre-trade liquidity, dealer and counterparty selection, and bond selection help drive efficiency in investor workflows.

You recently completed cloud migration to AWS. How was your experience with AWS?

Our overall experience has been extremely positive. The wide range of tools and services available in AWS had helped improve our IT efficiency, agility, and scalability. The ability to spin up whole environments on demand allows us to scale our development efforts and experiment with new technologies without incurring the cost of long-term contracts. Infrastructure scalability and resilience means that we can more efficiently execute on our mission to bring innovation to the credit market. 

What were the major pain points that forced you to migrate your corporate bond e-trading platform to AWS?

Our pain points were similar to the challenges that prompt most organizations to consider migrating to the cloud:

• Inability to rapidly scale infrastructure and inconsistent infrastructure costs
• Having to manage third party supplied services such as orchestration system and relational database management system (RDBMS)
• Time to implement managed services

More importantly we felt that the migration to deliver more value to our customers. By leveraging AWS services, we can offload operational tasks and focus more on innovation, allowing the team to build and deploy new features more quickly and efficiently.

You serve some of the biggest customer-centric organizations globally. Could you tell us about a unique user case scenario that highlights your agility and quality of service?

Seamless workflow is crucial for our client base, which includes buy-side asset managers. Screen real estate is very scarce for these traders. A top asset manager was looking to integrate our unique analytics and execution capabilities with their existing proprietary order management system (OMS). While we had not integrated with this system before, we had integrated with several third party OMS providers using standard protocols. Working with the client’s technical team, they were able to fully integrate LTX into their workflow in four weeks.

Recommended: AiThority Interview with Elliott Jobe, Chief Innovation Officer and Co-Founder at Infinite Reality

What’s the future of AI-assisted fixed income trading?

Enhanced liquidity discovery and improved prediction analytics: AI can help identify liquidity pockets in the fixed-income market, which is traditionally less liquid than equity markets. Improved liquidity discovery could lead to more efficient price discovery and facilitate better execution of trades. In addition to that AI is capable of processing vast amounts of data, leading to improved predictive analytics and enhancing identification of trading opportunities

Algorithmic trading strategies: As AI continues to improve, we could see an increase in adopting algorithmic trading strategies for fixed-income instruments. These strategies would leverage AI models to execute trades based on predefined criteria, aiming to optimize execution and reduce transaction costs.

Portfolio management and optimization: AI models can be increasingly used to optimize fixed-income portfolios, considering various factors such as risk tolerance, investment horizon, and market conditions. This would enable more effective portfolio construction and rebalancing.

Personalization: AI can be used to tailor fixed-income investment solutions to individual investors based on their unique risk profiles, investment objectives, and preferences.

Regulatory compliance and surveillance: AI-driven tools can be employed to monitor trading activities and ensure compliance with regulatory requirements. This could help identify and prevent market manipulation or other fraudulent activities in the fixed-income market

Any advice to CFOs on how to overcome challenging credit market risks?

As part of risk management processes, investment organizations need to execute trades, and often these trades involve illiquid securities. Given the lack of transparency in the corporate bond market, one of the biggest challenges market participants face is finding the other side of a trade. Our Liquidity Cloud was conceived to help solve the liquidity access challenge by allowing clients to find the natural counterparty for a trade. In instances when real-time natural contra liquidity is not available, our unique Similar Bonds analytics can be useful in helping identify similar tradeable alternative that meet the needs of a portfolio manager.

Recommended: AiThority Interview with Triveni Gandhi, PhD and Responsible AI Lead at Dataiku

Thank you, Jim ! That was fun and we hope to see you back on aithority.com soon.