AI is rapidly shifting from experimental add-on to coreinfrastructure in the retail trading industry, but brokers face a growingchallenge: how to harness its power without eroding trust or encouragingoverreliance.That tension framed a panel discussion at the FinanceMagnates Singapore Summit 2026, where executives from eToro, FXTrading.com,Bridgewise, and AI-focused firms debated how artificial intelligence isreshaping trading behavior, product design, and competitive dynamics acrossbrokerage platforms.The panel brought together, Vince De Castro, the Head ofMarketing at Acuity Trading, Adam Phillips, the CEO of FXTRADING.com, CarneyMak, Partner at FXHB Asset Management, Tuvshin Tug, the Founder at iC Candle, ThomasKareklas, the Director of Retail Forex Broker Division at BridgeWise, and YakiRazmovich, the MD for Singapore and Asia at eToro. From Feature to FoundationAcross the panel, there was broad agreement that AI is nolonger a peripheral tool. “It’s not a top layer,” said Kareklas. “It’s built within the core of all of our products.”That view was echoed by Phillips who described AI as part of the engine room of his brokerage after acquiringan in-house development team to control its technology stack and data inputs. “It's not just a bolt-on, it's a core part of the engineroom of our company that helps traders assess risk and trade effectively.”Similarly, Rasmovich said the platformhas become “AI-first,” embedding machine learning across the user journey, fromonboarding and trade selection to risk management.More from the event: “AI Very Useful for Fraud Detection, Monitoring”: FM Singapore Summit 2026 Enters Final DayThe shift reflects a wider industry transition: AI is nowcentral to how brokers structure client experience, generate insights, andultimately drive trading activity.Better Decisions, or Just More Trades?A key point of debate was whether AI genuinely improvesdecision-making or simply increases engagement. Panelists largely argued that AI enhances traders’analytical capabilities. By processing vast datasets, from macroeconomicindicators to earnings reports, AI tools can surface insights faster than manualanalysis. “It does the legwork,” said Rasmovich, noting that personalized AIagents can deliver real-time, tailored market intelligence based on a user’sportfolio and behavior.Mak framed AI as the mechanism that turns raw data into actionable signals.“Data is now quantifiable based on AI metrics… it not only complements analysisbut confirms it,” he said.“Data is now quantifiable based on AI matrix they discoverthey develop the AI and they give you solutions or suggestions to the AI andthat is the core the analytic core of AI. So, you know we can have all theinformation that CNBC or even Bloomberg shares on the CPI numbers and stuff butwho analyze them?”“Back then it was us with our own power knowledge and ourown education but now with AI, it not only complements that but they alsoconfirm that. So, you have a double confirmation and gives you a strongermotivation to put on a trade or more importantly put it on investment bet.”Yet there was also caution. Rasmovich warned that AI modelsremain rooted in historical data and may fail under unprecedented marketconditions. “Traders need to combine AI with their own judgment and duediligence,” he said.Behavioral Shift: From Reactive to StructuredPanelists agreed that AI is already changing how tradersoperate. Tug said AI is helpingshift traders from reactive behavior toward more disciplined strategies.Automated scanning and pattern recognition allow users to define criteria andlet algorithms identify opportunities, reducing time spent on manual chartanalysis.Related: AI Takes Center Stage in Brokers’ Layoff NarrativesAt the same time, AI is helping filter “noise”—a recurringtheme throughout the discussion. Bridgewise’s Thomas noted that curated,AI-driven insights can expand traders’ knowledge while simplifyingdecision-making, provided the data is reliable and regulated.However, this behavioral shift raises new risks. Fasterinsights and easier execution can encourage overtrading if not paired withproper safeguards.The Trust ProblemAs AI becomes more embedded, trust, and transparency, emergedas a central concern. “The goal shouldn’t be to trust AI blindly,” said Tug. “Itshould be to understand what AI is telling you and then decide.”Panelists emphasized the importance of explainability anddata integrity. Thomas drew a distinction between generic AI tools andpurpose-built financial systems, arguing that only the latter can provide“clean, audited” outputs suitable for trading decisions.Phillips highlighted another risk: AI systems designed to“please” users may generate misleading outputs, a known issue with largelanguage models. For brokers, this makes control over data sources and modelbehavior critical.What Won’t LastThere was clear consensus on what approaches are unlikely toendure. Using AI primarily as a marketing label drew sharpcriticism. “If brokers are using AI purely as a marketing tool, that won’tlast,” Phillips said, warning that superficial implementations will quicklylose credibility.Carney added that overloading platforms with multiple AItools can backfire, creating confusion rather than clarity. “If five differentAI tools give different suggestions, the trade will not be made,” he said. Panelists also pointed to earlier misuse of AI to driveclient churn and short-term volume, an approach increasingly at odds withregulatory expectations and long-term client retention.Differentiation in an AI-Saturated MarketWith AI adoption becoming ubiquitous, competitive advantageis shifting elsewhere. “It’s no longer a good-to-have, it’s a must,” said Rasmovich.Differentiation, he argued, will depend on the quality of algorithms,personalization, user experience, and integration across the trading lifecycle.Others pointed to control over infrastructure. Owning ordeeply understanding AI systems allows brokers to tailor outputs, incorporateuser feedback, and ensure consistency, advantages not easily replicated withoff-the-shelf tools.Localization also surfaced as a key factor. Carney notedthat trader preferences vary significantly across Asian markets, meaning AIdeployment must align with local trading cultures and behaviors rather thanfollow a one-size-fits-all model.A Tool, Not a Decision-MakerIn a closing exchange with the audience, the limits of AIbecame clear. Asked whether AI can determine a stock’s intrinsic value,Phillips offered a blunt assessment: “A stock’s value is where buyers andsellers meet… I haven’t met an AI yet that is effective at picking share pricemovements over the next two weeks.”The remark underscored a broader theme running through thesession: while AI can enhance analysis, streamline workflows, and improveaccess to information, it does not replace human judgment.For brokers, the challenge now is not adoption butexecution—embedding AI in ways that improve outcomes without undermining trust.For traders, the message was equally direct: AI may sharpen decisions, butresponsibility for those decisions remains firmly human.This article was written by Jared Kirui at www.financemagnates.com.