Artificial intelligence is changing how traders engage with fast-moving markets, and few people have watched this shift unfold as closely as Bryan Benson. After holding senior roles at Binance, he now leads Aurum, where AI-powered trading tools form the backbone of the company’s consumer-focused fintech platform.
Because crypto and FX trade nonstop, and much of today’s global order flow is already handled by algorithms, Benson believes retail traders face a built-in disadvantage. Human emotion, slower reaction times, and scattered data all make it harder for individuals to compete with automated systems.
In our conversation, Benson talked through why AI-driven strategies are starting to outperform human traders, how automation is reshaping market participation, and what retail users should keep in mind as AI becomes a more prominent part of everyday trading.
From your time at Binance to your work now at Aurum, what limitations can you say human traders consistently run into that AI can handle far more effectively?
From my time as a managing director at Binance to my current role as the CEO of Aurum, I keep seeing the same pattern: human traders are slow, emotional, and overloaded with information. Most retail traders cannot track dozens of pairs across several venues while following macro news and sentiment. Institutions solved this years ago with automation, and the majority of global trading volumes now rely on algorithms rather than manual execution. In U.S. equities, recent research and market data indicate that algorithmic systems now execute well over 60% of trading volume. As for the FX venues, machines already handle more than 70% of trading, demonstrating the profound impact of automation on the order flow today.
AI trading systems handle this complexity with consistent logic. They follow predefined risk rules, systematically size positions, and process new data in milliseconds at any time of day. There is no fatigue, no boredom, no impulse to “make it back.” That level of discipline and scale gives AI a structural advantage over even very experienced human traders, especially in fast crypto and FX markets.
AI reacts to market changes almost instantly. How significant is this speed advantage when compared with even the most disciplined human traders?
Speed matters most in the brief moments when the market dislocates, when news breaks, liquidity thins, and spreads widen. Algorithms read order-book changes and execute in milliseconds, while even a disciplined human needs seconds to understand the move and react. In markets already dominated by automated flow, those seconds create a meaningful gap in execution quality.
The real edge comes from combining that speed with robust risk controls. In Aurum’s AI bots, fast reaction sits inside a framework of predefined position sizes, clear take-profit and stop-loss levels, and continuous monitoring. We design the system so speed amplifies a disciplined, repeatable process that aims for incremental gains in people’s financial lives rather than impulsive decisions.
Emotional decision-making can often be the downfall of retail traders. How exactly do AI-driven strategies help remove those emotional biases from the process?
Retail traders often know basic rules yet abandon them under stress. After a loss, many increase risk, average into bad positions, or panic-sell winners. AI trading systems approach the same situation with fixed logic. They open, size, and close positions according to rules that remain constant regardless of mood, headlines, or recent outcomes.
Research on AI-powered mutual funds indicates that these funds trade less frequently, incur lower transaction costs, and avoid common behavioral biases, such as the disposition effect. They also tend to provide better downside protection in stressed markets compared with purely human-managed peers. At Aurum, we encode the same discipline into our strategies so the bot cuts out emotional errors and behaves consistently through winning streaks, losing streaks, and high-volatility periods.
Since crypto and FX never close, how much of a performance edge can AI give retail traders by staying active 24/7 when humans can’t?
Crypto and FX offer opportunities around the clock, so risk also exists around the clock. Human traders have to sleep, work, and live. AI systems can watch markets continuously, rebalance exposures, and react to price shocks or liquidity changes at any hour.
Continuous attention gives AI strategies a clear edge in capturing key inflection points. Overnight gaps, Asia–Europe handovers, and weekend news in crypto often drive sharp moves while most individuals are offline. A 24/7 AI approach lets people tap into an always-on financial system through technology that works in the background, helping to smooth blind spots and capture trends they would otherwise miss.
Many retail traders struggle because market data is fragmented across platforms. How do AI systems pull all of that together into something usable?
A well-built AI trading stack typically performs three key functions: it aggregates, cleans, and interprets data. The system pulls price, volume, and order-book feeds from multiple exchanges and FX venues, sometimes including derivatives, and normalizes those into a single view. Institutional desks already rely on execution platforms that combine pre-trade, real-time, and post-trade analytics for algorithm selection and performance review.
On top of this unified layer, AI models search for patterns such as momentum shifts, volatility regimes, and liquidity pockets that would be hard to detect on a single screen. In Aurum’s case, the output appears in a real-time dashboard so users interact with structured strategies and clear P&L. That experience replaces raw, fragmented market noise with an organized, usable view.
Institutional desks have relied on automation for years. How close are today’s consumer AI trading tools to offering retail traders similar sophistication?
The gap between institutional and retail technology is narrowing quickly. For years, only large banks and funds had the data and infrastructure to run advanced models, but the same core capabilities are now being productized for everyday users. Today, consumer platforms increasingly offer services that were previously available only for institutional customers, including multi-venue data, algorithmic execution, and machine-learning-based strategies, through straightforward mobile and web interfaces.
Institutional desks still enjoy significant advantages in data quality, research budgets, and risk infrastructure. However, in core behaviors such as strict rule-based execution and disciplined risk management, modern consumer AI tools are closing the gap. Aurum’s EX-AI BOT aims to give retail traders access to many of the same structural advantages that automated desks use, while keeping the user experience simple and accessible.
As AI tools become more accessible, do you see new risks emerging? Do you worry people will rely too heavily on automation?
New risks definitely emerge as people have started using AI tools to deploy complex strategies without fully understanding the risk embedded in the model. Researchers and regulators have already flagged concerns about overreliance on automated advice, biased data, and opaque algorithms in consumer financial products. At the same time, supervisors in the U.S. and Europe have begun targeting “AI-washing,” where firms exaggerate or misrepresent the role of AI in their products.
Healthy use of AI requires transparency and education. Users should know what the system does, which risks it takes, and under what conditions it may fail. At Aurum, we focus on clear dashboards, realistic expectations, and sustainable returns so automation becomes a disciplined partner in the process instead of an unexamined black box.
Looking ahead, how do you see AI transforming the relationship between traditional finance, decentralized finance, and retail traders over the next five years?
AI is turning into the connective layer between traditional finance, decentralized finance, and everyday investors. Large banks and asset managers already deploy AI to raise productivity and profits. Goldman Sachs estimates that generative AI could lift global GDP by about 7% over the next decade, and McKinsey projects $2.6–4.4 trillion in annual value from generative AI across industries. In parallel, DeFi continues to transform financial primitives into programmable building blocks.
Over the next five years, I expect AI agents to operate across both worlds, acting as digital teammates that can move value on our behalf. These systems will scan on-chain data, route orders through centralized and decentralized venues, optimize yield opportunities, and help with compliance workflows. Retail traders should see lower friction, more intelligent routing, and strategies tailored to their profile running on a unified, AI-enabled financial layer.
Finally, if a retail trader wants to use AI effectively, what mindset or habits should they adopt?
First, treat AI as leverage on a sound process rather than a shortcut to spectacular returns. Focus on steady, sustainable growth in your finances rather than chasing unrealistic monthly yields or “perfect” trades. Start small, understand the strategy you are automating, and focus on risk limits. Position sizing, diversification, and acceptable drawdowns deserve more attention than any single month of performance.
Second, stay engaged. Even the best AI system requires monitoring and periodic adjustment as market regimes change. Read the reports, understand what the bot is doing, and be willing to pause or tweak settings when conditions shift. Finally, approach marketing claims with skepticism, especially when they promise extremely high monthly yields. Sustainable AI-driven trading aims for steady, compounding results that match your risk tolerance and time horizon.
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