AI Promises Precision in Trading — While Delivering Bias on the Side

New AI-powered trading tools are popping up everywhere you look. They’re reshaping financial market participation at lightning speed, promising efficiency and accuracy. But behind the dazzling promises of AI trading lurks a silent risk: data bias. Some of these underestimated challenges could lead traders and brokers down a path of unexpected financial hazards, amplified systemic risks, and significant regulatory scrutiny.

When Algorithms Amplify Bias

Quantitative analysts have long warned that, despite their technological sophistication, AI systems still depend fundamentally on the quality and impartiality of their underlying data. Gappy Paleologo, a top hedge fund expert and partner at Balyasny Asset Management, recently stressed that AI trading algorithms inherently lack human "grounding," meaning they often fail to grasp real-world nuances critical to accurate forecasting.

There is a recognized risk that sophisticated AI models can overemphasize recent market data, exhibiting a form of recency bias that leads them to echo short-term momentum rather than true predictive insights - an issue contemporaneously highlighted by critics of AQR’s quant research and scholars at the University of Chicago.

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Additionally, Sergey Ryzhavin, head of B2COPY and with 15 years’ experience building AI-based trading systems, notes that while AI is adept at identifying historical trends, it struggles when faced with novel crises or events outside its training data, underscoring the importance of integrating human judgment into investment decisionsInstead, these tools can amplify historical biases, potentially delivering skewed results under volatile market conditions.These observations aren’t meant to discourage the use of AI-driven tools, but rather to raise awareness of their potential limitationsSergey Ryzhavin, Head of B2COPY (Photo: LinkedIn)

The Regulatory Spotlight

The ethical implications and financial risks associated with AI trading bias have not gone unnoticed by regulators. The European Union's new AI Act and existing GDPR frameworks require transparent and accountable AI systems. Jamie Dimon of JPMorgan has been vocal about the need for transparency, urging brokers and fintech firms to move away from opaque "black box" models toward fully auditable systems.

Industry experts largely agree that regulation must evolve in tandem with the increasing complexity of AI.“AI trading models are far more adaptive and opaque than traditional algorithms,” said David Belle, founder of The Fink Academy. “Without strong controls and ongoing oversight, an AI model could quickly escalate risks or make decisions that breach compliance or ethical standards. Higher standards aren’t about stifling innovation - they’re about ensuring these powerful systems don’t undermine market integrity or operational stability.”David Belle, Founder of The Fink Academy

Belle believes regulators should go further: “It would be helpful for requirements to scale with the potential impact of the model. High-risk systems should face more stringent documentation, stress testing, and real-time monitoring. There’s a clear gap in consistent standards for real-time supervision and automatic intervention when AI breaches predefined risk thresholds.”

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Regulators are sharpening their focus on the systemic risks posed by AI in financial markets. In April 2025, the Bank of England’s Financial Policy Committee cautioned that increasingly autonomous models used in trading may eventually learn that market stress events present profit opportunities, potentially worsening volatility during times of instability. The report highlighted that such systems might “identify and exploit weaknesses… for profit” and even engage in behavior that resembles collusion or manipulation, without any explicit human instruction.

Meanwhile, the Financial Conduct Authority has expressed concern that the pace of AI development may outstrip regulators’ ability to adapt, warning that autonomous trading systems could challenge oversight mechanisms and the integrity of fair markets.

A significant report from Finance Watch also calls for stricter standards for data audits, emphasizing that brokers must demonstrate more proactive risk mitigation. According to the report, if left unchecked, biased algorithms could inadvertently engage in AI-driven collusion, disrupting liquidity and market fairness, a scenario also extensively studied by researchers at Wharton-Penn.

Algorithmic Collusion: The New Frontier

Algorithmic collusion refers to a phenomenon where AI systems, particularly those used in competitive financial environments, learn to engage in anti-competitive behaviors without any explicit agreement or human intent. Through constant interaction, algorithms may unintentionally begin coordinating actions, such as synchronizing bids or pricing strategies, inadvertently creating herding effects and distorting market behavior.

Researchers distinguish between two types: “algorithmic collusion through intelligence,” where AI learns optimal collusive behaviors, and “algorithmic collusion through artificial stupidity,” where even unsophisticated models can produce destabilizing effects in noisy environments. In both cases, the collective behavior of multiple AIs is key to understanding the potential for systemic risk.

A high-noise environment is characterized by markets that are driven more by speculation, sentiment, and randomness than by fundamental data. Price signals can become unreliable very quickly, and AI models, particularly those trained solely on historical patterns, struggle to adapt effectively.

Recent simulations have shown that even unsophisticated reinforcement-learning bots can learn to collude without coordination or communication, reducing liquidity and worsening price accuracy while generating supra-competitive profits for their operators. Tom Higgins, CEO of Gold-i

Tom Higgins, CEO of fintech infrastructure provider Gold-i, has already seen scenarios play out in the real world. “Concerns around herding behavior and unintended collusion have increased the emphasis on intelligent risk management platforms,” he said. “Everything happens faster now than before AI. Risk decisions that used to take hours now need to be made in minutes, if not seconds.”

Despite rising awareness, regulatory frameworks remain ill-equipped to monitor or penalize unintentional collusion. The opacity of these models makes it difficult for firms to provide adequate disclosures, and regulators face what scholars call the “problem of many hands”. A situation in which harm or failure results from the actions of many individuals or systems, but no single person or entity can be clearly held responsible.Michael Osborne, a Professor at University of Oxford and co-founder of Mind Foundry

Professor Michael Osborne (University of Oxford, and co-founder of Mind Foundry) has warned that “it was … an illusion to think that data is neutral and objective.” He further cautioned about the trade-off between performance and transparency, asking “to what degree an AI should be able to explain itself.”

As AI complexity grows and the homogenization of trading algorithms increases, watchdogs such as the SEC, the EU Commission, and Finance Watch are urging tighter audit controls, enforceable explainability standards, and stricter accountability for brokers and developers.

Brokers as Guardians: Embracing Stewardship

Amid these growing risks, senior brokerage figures are increasingly advocating a responsible approach - integrating hybrid models that combine AI insights with human judgment to manage complex and unforeseen market scenarios effectively.

This responsibility entails conducting regular, comprehensive audits of data pipelines, performing meticulous stress-testing of algorithms under black-swan conditions, and maintaining explainable decision logs. None of these suggestions is simple, but they are becoming increasingly important.

Tom Higgins emphasized that education plays a crucial role in enabling firms to assume this responsibility.“The three most important things in trading are education, education, and education. I don't think standardisation is something regulators should focus on, but logging and auditing should absolutely be in their domain. When an AI system makes a decision, it's important to know why.”

Navigating the Road Ahead

With AI adoption rising sharply, for instance, UK firms leapt from 9% in 2023 to roughly 22% in 2024 (with the trend showing no signs of abating), oversight mechanisms are struggling to keep pace.

As the OECD notes, AI adoption in finance is part of a broader acceleration in uptake across industries, with recent surveys showing significant impacts on job tasks and operations in the sector. Meanwhile, the IOSCO 2025 Consultation Report indicates that AI is increasingly embedded in core market functions, including algorithmic trading, robo-advisory services, market surveillance, and compliance systems across global markets.

The road ahead calls for clear-eyed awareness of AI’s limitations, a strong ethical foundation, and a commitment to transparency. Yes, these are overused buzzwords that often lack meaning, but in this context, they serve as the building blocks of credibility and long-term stability in modern financial markets.

As echoed by the industry leaders interviewed for this piece, the key to harnessing AI lies not only in smarter models but in smarter oversight. This includes regulatory clarity, internal education, real-time risk visibility, and a shared responsibility to ensure AI doesn’t outpace accountability.

Brokers who take ownership of their role in managing these new risks can lead the way. By addressing data bias head-on, they’ll improve decision-making, meet rising regulatory expectations, and earn the trust of clients who are paying close attention.

We cannot slow technological advancement - that is without question.

Perhaps, this new era of AI-driven trading requires us to remain vigilant, understand the mechanisms behind the scenes, and not accept everything at face value. The real edge may lie in understanding hidden risks and advantages and making the best of them.