AI is rapidly shifting from experimental add-on to core
infrastructure in the retail trading industry, but brokers face a growing
challenge: how to harness its power without eroding trust or encouraging
overreliance.
That tension framed a panel discussion at the Finance
Magnates Singapore Summit 2026, where executives from eToro, FXTrading.com,
Bridgewise, and AI-focused firms debated how artificial intelligence is
reshaping trading behavior, product design, and competitive dynamics across
brokerage platforms.
The panel brought together, Vince De Castro, the Head of
Marketing at Acuity Trading, Adam Phillips, the CEO of FXTRADING.com, Carney
Mak, Partner at FXHB Asset Management, Tuvshin Tug, the Founder at iC Candle, Thomas
Kareklas, the Director of Retail Forex Broker Division at BridgeWise, and Yaki
Razmovich, the MD for Singapore and Asia at eToro.
Across the panel, there was broad agreement that AI is no
longer a peripheral tool. “It’s not a top layer,” said Kareklas. “It’s built within the core of all of our products.”
“Bullion Should Not Be Treated as Just Another CFD Product”: Insights from FM Singapore Summit 2026
FM Singapore Summit 2026 Day 1 Wrap-Up: A Strong Debut in Asia’s Leading Financial Hub
That view was echoed by Phillips who described AI as part of the engine room of his brokerage after acquiring
an 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 engine
room of our company that helps traders assess risk and trade effectively.”
Similarly, Rasmovich said the platform
has become “AI-first,” embedding machine learning across the user journey, from
onboarding and trade selection to risk management.
More from the event: “AI Very Useful for Fraud Detection, Monitoring”: FM Singapore Summit 2026 Enters Final Day
The shift reflects a wider industry transition: AI is now
central to how brokers structure client experience, generate insights, and
ultimately drive trading activity.
Contents
- 1 Better Decisions, or Just More Trades?
- 2 Behavioral Shift: From Reactive to Structured
- 3 The Trust Problem
- 4 What Won’t Last
- 5 A Tool, Not a Decision-Maker
- 6 Better Decisions, or Just More Trades?
- 7 Behavioral Shift: From Reactive to Structured
- 8 The Trust Problem
- 9 What Won’t Last
- 10 A Tool, Not a Decision-Maker
Better Decisions, or Just More Trades?
A key point of debate was whether AI genuinely improves From left: Vince Castro, Adam Phillips, Carney Mak, Tuvshin Tug, Thomas Kareklas, and Yaki Razmovich
decision-making or simply increases engagement. Panelists largely argued that AI enhances traders’
analytical capabilities. By processing vast datasets, from macroeconomic
indicators to earnings reports, AI tools can surface insights faster than manual
analysis. “It does the legwork,” said Rasmovich, noting that personalized AI
agents can deliver real-time, tailored market intelligence based on a user’s
portfolio 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 analysis
but confirms it,” he said.
Yet there was also caution. Rasmovich warned that AI models
remain rooted in historical data and may fail under unprecedented market
conditions. “Traders need to combine AI with their own judgment and due
diligence,” he said.
Behavioral Shift: From Reactive to Structured
Panelists agreed that AI is already changing how traders
operate. Tug said AI is helping
shift traders from reactive behavior toward more disciplined strategies.
Automated scanning and pattern recognition allow users to define criteria and
let algorithms identify opportunities, reducing time spent on manual chart
analysis.
Related: AI Takes Center Stage in Brokers’ Layoff Narratives
At the same time, AI is helping filter “noise”—a recurring
theme throughout the discussion. Bridgewise’s Thomas noted that curated,
AI-driven insights can expand traders’ knowledge while simplifying
decision-making, provided the data is reliable and regulated.
However, this behavioral shift raises new risks. Faster
insights and easier execution can encourage overtrading if not paired with
proper safeguards.
The Trust Problem
As AI becomes more embedded, trust, and transparency, emerged
as a central concern. “The goal shouldn’t be to trust AI blindly,” said Tug. “It
should be to understand what AI is telling you and then decide.”
Panelists emphasized the importance of explainability and
data integrity. Thomas drew a distinction between generic AI tools and
purpose-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 large
language models. For brokers, this makes control over data sources and model
behavior critical.
What Won’t Last
There was clear consensus on what approaches are unlikely to
endure. Using AI primarily as a marketing label drew sharp
criticism. “If brokers are using AI purely as a marketing tool, that won’t
last,” Phillips said, warning that superficial implementations will quickly
lose credibility.
Carney added that overloading platforms with multiple AI
tools can backfire, creating confusion rather than clarity. “If five different
AI tools give different suggestions, the trade will not be made,” he said. Panelists also pointed to earlier misuse of AI to drive
client churn and short-term volume, an approach increasingly at odds with
regulatory expectations and long-term client retention.
With AI adoption becoming ubiquitous, competitive advantage
is 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 or
deeply understanding AI systems allows brokers to tailor outputs, incorporate
user feedback, and ensure consistency, advantages not easily replicated with
off-the-shelf tools.
Localization also surfaced as a key factor. Carney noted
that trader preferences vary significantly across Asian markets, meaning AI
deployment must align with local trading cultures and behaviors rather than
follow a one-size-fits-all model.
A Tool, Not a Decision-Maker
In a closing exchange with the audience, the limits of AI
became clear. Asked whether AI can determine a stock’s intrinsic value,
Phillips offered a blunt assessment: “A stock’s value is where buyers and
sellers meet… I haven’t met an AI yet that is effective at picking share price
movements over the next two weeks.”
The remark underscored a broader theme running through the
session: while AI can enhance analysis, streamline workflows, and improve
access to information, it does not replace human judgment.
For brokers, the challenge now is not adoption but
execution—embedding AI in ways that improve outcomes without undermining trust.
For traders, the message was equally direct: AI may sharpen decisions, but
responsibility for those decisions remains firmly human.
AI is rapidly shifting from experimental add-on to core
infrastructure in the retail trading industry, but brokers face a growing
challenge: how to harness its power without eroding trust or encouraging
overreliance.
That tension framed a panel discussion at the Finance
Magnates Singapore Summit 2026, where executives from eToro, FXTrading.com,
Bridgewise, and AI-focused firms debated how artificial intelligence is
reshaping trading behavior, product design, and competitive dynamics across
brokerage platforms.
The panel brought together, Vince De Castro, the Head of
Marketing at Acuity Trading, Adam Phillips, the CEO of FXTRADING.com, Carney
Mak, Partner at FXHB Asset Management, Tuvshin Tug, the Founder at iC Candle, Thomas
Kareklas, the Director of Retail Forex Broker Division at BridgeWise, and Yaki
Razmovich, the MD for Singapore and Asia at eToro.
Across the panel, there was broad agreement that AI is no
longer a peripheral tool. “It’s not a top layer,” said Kareklas. “It’s built within the core of all of our products.”
“Bullion Should Not Be Treated as Just Another CFD Product”: Insights from FM Singapore Summit 2026
FM Singapore Summit 2026 Day 1 Wrap-Up: A Strong Debut in Asia’s Leading Financial Hub
That view was echoed by Phillips who described AI as part of the engine room of his brokerage after acquiring
an 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 engine
room of our company that helps traders assess risk and trade effectively.”
Similarly, Rasmovich said the platform
has become “AI-first,” embedding machine learning across the user journey, from
onboarding and trade selection to risk management.
More from the event: “AI Very Useful for Fraud Detection, Monitoring”: FM Singapore Summit 2026 Enters Final Day
The shift reflects a wider industry transition: AI is now
central to how brokers structure client experience, generate insights, and
ultimately drive trading activity.
Better Decisions, or Just More Trades?
A key point of debate was whether AI genuinely improves From left: Vince Castro, Adam Phillips, Carney Mak, Tuvshin Tug, Thomas Kareklas, and Yaki Razmovich
decision-making or simply increases engagement. Panelists largely argued that AI enhances traders’
analytical capabilities. By processing vast datasets, from macroeconomic
indicators to earnings reports, AI tools can surface insights faster than manual
analysis. “It does the legwork,” said Rasmovich, noting that personalized AI
agents can deliver real-time, tailored market intelligence based on a user’s
portfolio 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 analysis
but confirms it,” he said.
Yet there was also caution. Rasmovich warned that AI models
remain rooted in historical data and may fail under unprecedented market
conditions. “Traders need to combine AI with their own judgment and due
diligence,” he said.
Behavioral Shift: From Reactive to Structured
Panelists agreed that AI is already changing how traders
operate. Tug said AI is helping
shift traders from reactive behavior toward more disciplined strategies.
Automated scanning and pattern recognition allow users to define criteria and
let algorithms identify opportunities, reducing time spent on manual chart
analysis.
Related: AI Takes Center Stage in Brokers’ Layoff Narratives
At the same time, AI is helping filter “noise”—a recurring
theme throughout the discussion. Bridgewise’s Thomas noted that curated,
AI-driven insights can expand traders’ knowledge while simplifying
decision-making, provided the data is reliable and regulated.
However, this behavioral shift raises new risks. Faster
insights and easier execution can encourage overtrading if not paired with
proper safeguards.
The Trust Problem
As AI becomes more embedded, trust, and transparency, emerged
as a central concern. “The goal shouldn’t be to trust AI blindly,” said Tug. “It
should be to understand what AI is telling you and then decide.”
Panelists emphasized the importance of explainability and
data integrity. Thomas drew a distinction between generic AI tools and
purpose-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 large
language models. For brokers, this makes control over data sources and model
behavior critical.
What Won’t Last
There was clear consensus on what approaches are unlikely to
endure. Using AI primarily as a marketing label drew sharp
criticism. “If brokers are using AI purely as a marketing tool, that won’t
last,” Phillips said, warning that superficial implementations will quickly
lose credibility.
Carney added that overloading platforms with multiple AI
tools can backfire, creating confusion rather than clarity. “If five different
AI tools give different suggestions, the trade will not be made,” he said. Panelists also pointed to earlier misuse of AI to drive
client churn and short-term volume, an approach increasingly at odds with
regulatory expectations and long-term client retention.
With AI adoption becoming ubiquitous, competitive advantage
is 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 or
deeply understanding AI systems allows brokers to tailor outputs, incorporate
user feedback, and ensure consistency, advantages not easily replicated with
off-the-shelf tools.
Localization also surfaced as a key factor. Carney noted
that trader preferences vary significantly across Asian markets, meaning AI
deployment must align with local trading cultures and behaviors rather than
follow a one-size-fits-all model.
A Tool, Not a Decision-Maker
In a closing exchange with the audience, the limits of AI
became clear. Asked whether AI can determine a stock’s intrinsic value,
Phillips offered a blunt assessment: “A stock’s value is where buyers and
sellers meet… I haven’t met an AI yet that is effective at picking share price
movements over the next two weeks.”
The remark underscored a broader theme running through the
session: while AI can enhance analysis, streamline workflows, and improve
access to information, it does not replace human judgment.
For brokers, the challenge now is not adoption but
execution—embedding AI in ways that improve outcomes without undermining trust.
For traders, the message was equally direct: AI may sharpen decisions, but
responsibility for those decisions remains firmly human.
