Retail trading platforms were built to connect users to markets. For a long time, that was enough. But things have changed. Algorithmic systems now handle most of the order flow across crypto, FX, and equities, and retail traders are competing against machines without any of the tools that might level the playing field.
Bryan Benson has watched this gap widen from both sides. During his time as Managing Director at Binance, he saw millions of users trade on platforms that gave them market access but no real edge. The infrastructure that institutions relied on simply didn’t exist for retail. Now, as CEO of Aurum, he’s focused on building the AI infrastructure layer he believes retail platforms have been missing all along.
1. Bryan, drawing on your experience as Managing Director at Binance, where did you see the most significant technology gaps in retail trading infrastructure?
The biggest gap was always the quality of execution itself. Retail platforms gave users access to markets, but the infrastructure behind the scenes looked nothing like what institutions had. Smart order routing didn’t exist for retail. Neither did real-time risk engines nor any systematic way to process data at scale.
At Binance, I watched millions of users trade on platforms that were essentially order forms connected to an exchange. They could buy and sell, but they had no edge. Meanwhile, institutional desks ran algorithms that scanned multiple venues, optimized entry points, and managed risk automatically. Retail never had access to any of that.
Data was the other problem, and in some ways an even bigger one. Institutions aggregate feeds from dozens of sources and run models on top of them. Retail traders get a price chart and maybe some basic indicators. That asymmetry explains a lot of why most retail traders lose money.
2. From a market-structure perspective, what lessons from running operations at one of the world’s largest exchanges now shape your belief that AI must become a core infrastructure layer for retail platforms?
What really shaped my thinking was watching the order flow and realizing who was on the other side. Most of the order flow on major exchanges already comes from automated systems — in crypto, in FX, in equities. Manual traders are competing against machines that react in milliseconds and never get tired.
That’s not a fair fight, and the numbers reflect it. Retail loss rates are high because the infrastructure gap keeps getting bigger. Institutions keep building faster, smarter infrastructure, while most retail platforms focus on adding new coins and hope that’s enough.
If retail platforms want their users to have a real chance, they need to embed the same systematic advantages that professional desks rely on: execution logic, risk management, and data processing — all of it running in the background so that the users don’t have to think about it.
3. For years, advanced execution engines were available only to institutions. Can AI-driven infrastructure now give retail crypto platforms access to the same level of trading sophistication?
From a technical standpoint, everything you need to build institutional-grade systems is already available. Cloud infrastructure, exchange APIs, and open-source ML tools have made it possible to build high-end systems without institutional budgets. Ten years ago, this required a small army of quants and serious hardware. That’s no longer the case.
The real question is whether retail platforms actually choose to build this way. Most don’t, because it’s easier to ship a simple trading interface and collect fees. The ones that invest in proper AI infrastructure can offer users something genuinely different.
At Aurum, we built around this idea from the start. Our team came from Binance, Morgan Stanley, IBM, and OKX, and they brought the same standards they applied at those institutions. Retail platforms can absolutely close the gap now, as long as they build AI into the foundation and don’t just tack it on for marketing.
4. Aurum positions AI as a 24/7 backbone that uncovers mid-term and real-time opportunities that manual systems miss. What types of patterns or signals does your system detect?
Our approach is to look at multiple layers of data simultaneously, because no single source gives you the full picture: price and volume behavior across exchanges, order book depth, liquidity shifts on DEXs, and whale wallet movements on-chain.
The mid-term signals come from pattern recognition across market cycles. The models learn which setups tend to precede sustained moves and which ones fade. That takes years of historical data and constant validation against live conditions.
Real-time signals are more about speed: arbitrage windows between exchanges, sudden liquidity imbalances, and sentiment spikes around news events. These opportunities last seconds or minutes at most, and while a human trader might catch one occasionally, the AI picks them up systematically.
5. How are these patterns translated into practical, AI-driven trading strategies?
AAVE’s flash loan protocol is what makes the execution possible. When the AI spots a price gap between DEXs, it borrows capital, buys on the cheaper venue, sells on the pricier one, repays the loan, and pockets the spread — all in a single transaction. The loan only goes through if the math clears after fees. No directional bets, no overnight exposure. If any step fails, the transaction reverts and we’re out nothing but gas.
From the user’s perspective, none of this complexity is visible. They see results in their dashboard and don’t need to understand flash loans or DEX routing to benefit from them.
6. Retail performance is often hurt by emotional decision-making. How does an AI execution layer help remove behavioral bias and maintain discipline during volatility?
The key is that users never face the decision in the first place. Traditional platforms put every choice in front of the trader. Hold or sell? Add to the position or cut it? Those decision points are where emotion creeps in.
When AI handles execution at the infrastructure level, those moments don’t exist for the user. The system already knows entry conditions, position sizing, and exit rules. Volatility hits, the parameters execute, and the user sees results after the fact.
Our flash loan model takes this even further. Trades open and close within a single transaction, so there’s never a position sitting there while someone second-guesses whether to hold. The emotional window simply doesn’t exist.
7. Looking ahead, how do you expect the AI infrastructure layer for retail traders to develop over the next few years?
Platforms that don’t build AI into their infrastructure will lose users to those that do. It’s that simple. When users get used to a platform that handles execution, risk, and opportunity detection on its own, they won’t want to go back to doing it manually.
I expect the infrastructure layer to become standardized over time. Right now, each platform builds its own stack. In a few years, you’ll likely see modular AI components that any exchange or broker can plug in. That will raise the floor for everyone, which is good for retail traders overall.
The interesting battle will be at the edges. The base layer gets commoditized, so platforms compete on what they build on top: better data sources, smarter signal filtering, and tighter DeFi integrations. That’s where Aurum is focused. We already have the flash loan infrastructure working. Now we need to make every piece around it sharper and faster than what anyone else offers.
