How artificial intelligence is reshaping research, risk management and execution for everyday investors.
AI is no longer a futuristic complement to DIY investing. It becomes the operating system that powers it.
As part of our monthly AI in Wealth spotlight, InvestmentNews asked Anthony Denier, CEO of Webbullfor his perspective on how technology is transforming the capabilities of self-directed investing platforms, to help advisors understand how this competitive part of the market is evolving.
Denier describes the acceleration and reshaping of how everyday investors study markets, interpret signals and execute trades in an industry moving from autonomous navigation to AI-guided and aided decision-making.
Just two years ago, retail investors were largely building their own workflows. They searched for data, interpreted it, selected instruments, executed trades, and managed risks manually. According to Denier, AI has reduced this process into a single continuous loop.
“The biggest change is that the platform can translate data and intent into information and an action plan in real time,” he explains. “You can ask a question in plain language, get context specific to the symbol and your position, and trade with risk and market context already framed. This shift shifts decision-making from manual research and platform navigation to guided execution with guardrails and potential consequences immediately visible.”
AI and the globalization of retail investing
Retail has always been limited by geography, time zones and unfamiliar market conventions. Denier says AI dissolves much of this friction.
“AI makes global participation appear continuous rather than segmented,” he says, explaining that Webull’s AI systems translate market conventions and overnight developments into a consistent user experience. “This helps users understand how a movement in one region affects correlated assets elsewhere, so they can respond without being experts in each area.”
However, some problems cannot be overcome by technology alone, Denier acknowledges.
“The remaining barriers are structural. Market access, product availability, liquidity, tax treatment and jurisdiction-specific restrictions still determine what a retail investor can trade and when. AI can explain, frame and route, but it cannot eliminate regulatory and structural market constraints,” he says.
Design AI to reduce noise, not add to it
As AI unleashes huge streams of data in real time, platforms risk overwhelming users. Denier says the solution lies in disciplined information design.
“The balance comes from processing information as a step-by-step system and consolidating the results into one concrete response,” he says. “The default experience should be simple: one or two high trust signals, a clear “why this matters,” and a next best action. »
Depth is only provided when necessary, notes Denier: “The AI should first summarize, then expand only when requested or when risk demands it. If the user does not hold the asset and does not intend to trade, it does not need institutional-level details. If it is in a leveraged position, it does so. The platform’s job is not to overwhelm users with everything they know. It it’s about showing the minimum amount of information necessary so they can make smarter decisions without surprises.”
AI as an interpreter and guardian of risks
For new investors, global markets can seem chaotic. Denier describes the role of AI as both translator and guardian of risks.
“AI acts as an interpreter and advocate when it comes to security,” he explains. “AI can translate macroeconomic headlines, central bank actions, earnings surprises, and volatility changes into plain language, but it must also label uncertainty and separate fact from interpretation. Responsible use involves teaching users what a signal typically affects, over what time horizons it operates, and what might invalidate it.”
Rather than predicting outcomes, AI should manage behavioral risks: “The goal is not to provide predictions to the user, but to avoid overconfidence and over-complication. »
Where human judgment still matters most
As automation increases, Denier sees a clear separation of responsibilities between machines and investors.
“The relationship becomes a division of labor. Algorithms manage the detection, monitoring and execution of mechanisms. Humans retain their intentions, constraints and responsibilities,” he says. “AI can surface patterns, flag risks, and automate repetitive steps, but it should not replace user choice, goals, or risk tolerance. »
He says transparency is key: “The best systems will make the transfer explicit: ‘Here’s what I see, here’s the assumptions, here’s the risks, here’s the trade-offs expressed as parameters.’ Then the human confirms. Platforms that do this well will preserve human priority, transparency and explainability post-trade.
Integrate Compliance into the Architecture
Operating in multiple jurisdictions requires careful regulatory design, particularly when AI influences financial decisions.
“We treat compliance as a product architecture, not a review step,” says Denier. “AI needs jurisdiction-specific policies: what can be shown, what can be suggested, what information is required, what records must be kept, and what monitoring controls apply. This means separating “education and explanation” from “recommendation” and imposing a separation between model results and user interface.
Consistency across borders is difficult but achievable, he says: “The right approach is a single central system with layers of modular rules per region, as well as ongoing monitoring, testing and documentation so that regulators can understand how the system behaves. »
The rise of the co-pilot of adaptive trading
Looking from a three to five year perspective, Denier envisions trading platforms evolving beyond tools to become intelligent companions.
“The experiment we are running is closer to an adaptive trading co-pilot than an application full of tools. The AI will be persistent, proactive and sensitive to the portfolio: it will explain what has changed overnight, simulate the results, warn you when risk creeps in and convert a goal into a structured plan across several instruments,” he envisions. “Commerce and education are merging into one indistinguishable flow. Success will depend as much on discipline as it will on innovation. The platforms that succeed will be those that combine three things: trust, quality of execution, and restraint.”
As AI integrates into every level of retail investing, Denier’s message is that intelligence must be combined with accountability, automation with transparency, and innovation with trust. The future of trading, he believes, is not only smarter, but also more guided, more contextual, and ultimately more human in the way it enables choices.
