pool. Tis is our specialism we the years of experience in market making. Te one blocker for us to provide this healthy liquidity is prediction market fees and especially in a situation where the fee structure is not the same for each client. We understand platforms need to earn revenue, but it needs to be a level playing field to provide the best product to the customer.
How do your proprietary AI-powered market-making models differ from traditional sports betting or exchange pricing engines?
but I would be confident it is a challenge the operators can overcome. I think another area that is inefficient is the lack of depth in terms of market offering. Tis is a problem we can solve, which is why we are investing in pricing more markets that have historically been very retail focused rather than sharp pricing such as player props and same game parlays.
Stephen Shaw and Enda Kendrick bring deep trading and exchange expertise. What capabilities do they unlock?
Te uncertainty around regulation is probably stopping bigger hedge funds from investing in the space, who are currently sitting on the fence and assessing how the
landscape evolves. Tis uncertainty matters less to groups like us who were active before and operate globally across multiple markets.
Artificial intelligence is central to our modelling approach. We’ve built a suite of fundamental models using advanced machine learning techniques that form the foundation of our odds creation, enabling us to process large volumes of data with speed and precision. We complement this with over 50 traders across 35 countries worldwide. Tis data is then utilised by a senior trader to establish the fair price at which we will trade.
Te difference between us and a typical sportsbook trading team is fundamental as we form a proprietary view on what the price should be and then actively trade and market make based on that view. A typical sportsbook trading team, in contrast, will trade in an automated manner, merely following price moves in the broader market.
Tese market price moves are generally powered by data from groups like ours. Furthermore, our model is built to sustain sharp, anonymous flow. A typical sportsbook, and even some betting exchanges, need to profile and restrict their customers to remain profitable.
What specific operational or product inefficiencies do you see in today’s prediction market exchanges?
Te main inefficiencies I see are the platforms are new and objectively not on par with retail bookmakers. Tis is not an area we can influence
ALEXANDRE LUNEAU Co-Founder and CEO Moon Intelligence
Te combination of the trading and exchange experience coming into Moon Intelligence right now is perfect to assist us with growing our US strategy. Stephen brings a wealth of experience from tech-heavy sports trading and data environments, most recently at Jump Trading. He has led and scaled teams and operations that rely on advanced data pipelines, AI/ML, and low latency trading infrastructures, giving him a clear, practical view of what it takes to make these systems work in production.
As we build out a fully end-to-end technology solution that enables market makers to operate programmatically on prediction market exchanges, his experience directing the design and operation of automated trading and risk systems is invaluable in shaping a robust, scalable market-making platform.
Enda has spent the majority of his career working with UK based betting exchanges, giving him an in-depth understanding of the liquidity challenges prediction markets face. He has worked closely with market makers to solve these challenges within the UK market. Tis experience is exactly what we need to help us provide our US clients with the solutions their platforms require.
Enda is already providing valuable advice to our US clients regarding specific platform challenges they have encountered.
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