Preface
As artificial intelligence (AI) is consistently reshaping the global trading behavior, its influence on crypto markets is becoming impossible to ignore. In this exclusive interview, BlockchainReporter.net speaks with Bryan Benson , CEO of Aurum, a fintech professional with nearly three decades of experience across digital assets and exchange infrastructure.
Benson discussed his perspective on why AI-led, emotionless execution may define the next bull market. In addition, he also discusses that how retail traders can close the gap with institutions, where human judgment still matters in an increasingly automated financial future.
As Aurum ‘s CEO, he’s building tools that connect trading algorithms, DeFi, and payment systems — technology he believes will define the next bull cycle. But the market doesn’t care about vision alone. In this conversation, Benson explains why execution will separate winners from noise.
Interview Session
Based on your decades of experience in fintech, how would you reflect on the shift from traditional trading to AI-driven trading within the retail sector?
When I began, “technology” in trading basically meant a faster terminal and a better data feed. Most retail investors still relied on intuition, media noise, and a handful of basic indicators. Today, the space looks very different: a majority of global equity volume is executed algorithmically, by high-frequency or algorithmic traders.
Institutional asset managers have already embraced this shift. Many are deploying AI across research, allocation, and portfolio construction. By contrast, retail investors are only now catching up. Historically, many have underperformed simple index strategies, often lagging by a few percentage points annually due to overtrading and poor timing.
What has really changed is that tools once reserved for hedge funds are being reimagined for everyday users. At Aurum, we integrate AI-powered trading solutions to access institutional-style execution alongside everyday financial utility.
What are the leading emotional biases traders face during bull rallies, and what is AI’s role in neutralizing such weaknesses?
It’s easy to point to fear and greed, but in reality, a broader assortment of behavioral traps underlies retail underperformance during rallies. Overconfidence, herd behaviour, FOMO, and the reluctance to realize losses are recurring pitfalls. Especially in fast rallies, many investors end up doubling down emotionally, then freezing or panicking when volatility returns.
AI doesn’t erase human irrationality, but it allows a form of behavioural pre-commitment. A well-designed system can bake in position sizing, stop-loss rules, profit-taking logic, and diversification long before emotions enter the picture. The algorithm doesn’t experience envy when a coin moons, or panic when the chart turns red. It simply executes predefined rules.
What we built at Aurum is not some reckless “pump-chaser,” but a disciplined framework: users enjoy data-driven execution while still controlling their risk appetite and goals. In that sense, AI acts less like a crystal ball and more like a seatbelt. It won’t stop volatility, but it might prevent a catastrophic crash when emotions run wild.
How do advanced AI-powered trading models react rapidly to volatility compared with a human trader’s response speed?
Human traders, no matter how skilled, are limited by attention span, cognitive load, and the demands of sleep, off-hours, or everyday life. You might monitor a few charts, maybe dozens of assets, and your response time is measured in seconds or minutes.
Algorithms operate on a radically different time scale. They can scan thousands of order books, parse complex data across multiple assets, and react in milliseconds. This mode now handles a major share of trading volume.
Crypto markets only amplify the need for automation; they never rest. There are no closing bells or trading windows. An AI-driven system can monitor funding rates, cross-asset correlations, volatility spikes, and order flow around the clock. As well as adjust exposure dynamically instead of waiting for someone to wake up and scroll through price charts.
Our AI-Trader EX bot was created for exactly that environment. It runs continuously, analyzes real-time data, and executes a diversified set of spot strategies with embedded risk management. This gives the user the potential performance of a full trading desk without needing to stare at screens all day. Internal back-testing emphasizes consistent, risk-adjusted results, not headline-grabbing bets.
While some critics assert AI eliminates the element of “human intuition” from trading, do you think intuition will have any place in the upcoming bull run?
Intuition isn’t going away; it just evolves upstream. In classic retail trading, intuition lived in every buy or sell click: when to enter, when to exit, whether to hold, when to panic. In an AI-first world, intuition becomes more about designing the system: choosing which data streams matter, interpreting macro developments, deciding when to override or halt a model.
From my experience at Binance to building Aurum now, I’ve learned that the strongest outcomes flow when human judgment and machine execution are clearly separated. Humans still provide context (regulatory shifts, macro signals, evolving narratives), and machines execute with precision, discipline, and speed.
That’s why I feel that, in the next bull run, the real edge will come from “better instinct about building, supervising, and evolving the systems” to respond to changing conditions.
According to you, what is AI-led trading’s role in shaping market depth, liquidity, and overall market health in a likely bull cycle?
AI-led trading can significantly deepen order books, tighten spreads, and provide liquidity precisely when human participants hesitate. In traditional markets, HFT and algo trading already supply a significant portion of intraday volume and liquidity provision.
When responsibly applied to crypto and digital-asset markets, similar techniques can reduce slippage, make market entry and exit smoother for retail traders, and support more efficient market mechanics.
That said, if many actors deploy similar models chasing similar signals, you risk crowded trades or abrupt liquidity withdrawals. This can lead to the creation of “air pockets” where the market suddenly thins, now happening at machine speed instead of human hesitation. Automated trading doesn’t erase behavioral finance; it amplifies it.
That’s precisely why Aurum emphasizes risk management, diversification, and multi-channel liquidity. We envision an AI-assisted financial ecosystem that links trading algorithms with DeFi tools (flash-loan arbitrage, staking, and payments), so that liquidity flows through multiple channels, not a single speculative engine.
What are the present AI trading mechanisms’ limitations, and what breakthroughs could pave the way for a truly independent trading environment?
The first limitation is data and regime-change risk. Most models are trained on historical patterns, but crypto markets evolve, and quiet periods can swiftly morph into regulatory or macro-driven storms. A system that worked flawlessly under one regime might cease to perform under another, particularly if you treat AI as “set and forget.”
Second is opacity. Many AI systems behave like black boxes. For finance, that’s a serious challenge. AI’s value as an investment tool remains under scrutiny, and very few funds incorporate AI or machine learning explicitly in a formally governed way.
Because of this, I don’t believe in a fully independent, human-free trading environment. Instead, I foresee more automation around execution, risk controls, and rebalancing. That can be supplemented by improved explainability, on-chain analytics, multi-asset data fusion, and real-time monitoring — all while retaining human governance, compliance, and ethical oversight.
What is the future of cutting-edge “emotional finance” in fortifying the retail landscape?
If the last decade was about giving retail investors access to markets, the next will be about giving them access to their own psychology. It is understood that biases like loss aversion, herding, or overconfidence contribute meaningfully to retail underperformance.
Emotional finance, as we see it, is about building tools that help users recognize their own patterns, counteract destructive impulses, and make more disciplined decisions. Practically, that means user interfaces that surface risk in intuitive ways, paired with AI-driven execution that enforces discipline.
My hope is that “emotionless finance” does not feel cold or alien. Instead, it uses technology to remove the most self-defeating impulses, so that retail investors can focus on long-term goals, disciplined risk-taking, and genuine wealth creation.
Concluding Remarks
As crypto markets heading toward their next expansion phase, Benson’s insights underline a clear shift as success may depend less on instinctive trading and more on disciplined, AI-driven execution. While human judgment still matters and guides about the strategy and oversight, the coming bull cycle appears set to reward those who pair emotions with intelligent automation.
