The Hidden Risks of AI in Finance: How Behavioral Biases in LLMs Threaten Algorithmic Trading and Investment Platforms

The rise of artificial intelligence in financial markets has been hailed as a revolution in efficiency and precision. Yet, beneath the surface of algorithmic trading and AI-driven investment platforms lies a growing concern: the embedded behavioral biases of large language models (LLMs) could amplify financial risks, distort market dynamics, and undermine investor trust. Recent research reveals that LLMs, despite their sophistication, mirror human cognitive flaws such as recency bias, authority bias, and anchoring effects-biases that can skew stock predictions, reinforce suboptimal strategies, and even destabilize markets, as noted in a 2024 arXiv preprint.

The Biases Embedded in AI: A Mirror of Human Flaws

LLMs trained on vast financial datasets inherit patterns that reflect historical human behavior, including irrational tendencies. For instance, recency bias causes models to overreact to recent data, such as quarterly earnings reports, leading to exaggerated forecasts about stock price movements, as described in the 2024 arXiv preprint. A 2024 study of open-source vision-language models like LLaVA-NeXT and Mini-Gemini found that these systems disproportionately prioritize the latest market news, often amplifying short-term volatility, as noted in the 2024 arXiv preprint. Similarly, authority bias manifests when LLMs disproportionately weight statements from high-profile figures like Warren Buffett or Ray Dalio, potentially overriding objective analysis, as described in the 2024 arXiv preprint.

Anchoring bias further complicates decision-making. When prompted with prior high or low values, models like GPT-4 and Gemini Pro adjust their forecasts accordingly, even when the anchor is arbitrary, as shown in a 2024 ScienceDirect study. Techniques such as "Chain of Thought" reasoning have shown limited success in mitigating this bias, underscoring the challenges of aligning AI with rational decision-making frameworks, as noted in the 2024 ScienceDirect study.

Algorithmic Trading: A Double-Edged Sword

The implications for algorithmic trading are profound. LLMs deployed in trading strategies often exhibit sector, size, and momentum biases, favoring technology stocks and large-cap companies while neglecting less visible industries, as reported in a 2025 arXiv preprint. This tendency creates confirmation bias loops: models persist in their initial judgments despite contradictory evidence, reducing adaptability in volatile markets, as noted in the 2025 arXiv preprint. For example, an LLM optimized for momentum trading might overvalue a trending tech stock while undervaluing a stable utility company, even when macroeconomic indicators suggest otherwise, as described in the 2025 arXiv preprint.

These biases are exacerbated by the architectures of deep learning models. Recurrent neural networks (RNNs) and long short-term memory (LSTM) systems, commonly used in financial predictions, inherit LLM biases through their training data, leading to skewed outputs, as noted in a 2025 ScienceDirect review. A 2025 review of deep learning applications in trading noted that such models often "overfit" to historical patterns, failing to account for structural market shifts, as reported in the 2025 ScienceDirect review.

Regulatory Responses: Lagging Behind the Technology

Regulators are only beginning to grapple with these risks. The U.S. Securities and Exchange Commission (SEC) has acknowledged the growing use of AI in capital markets but has yet to finalize comprehensive rules addressing LLM-driven investment risks, as outlined in a 2025 CRS report. In 2025, the SEC launched an AI roundtable to discuss issues like "AI washing" (the misleading promotion of AI capabilities) and market collusion risks, but concrete policy frameworks remain elusive, as noted in the 2025 CRS report.

The Office of Management and Budget has encouraged agencies to accelerate AI adoption, including using AI to monitor trading activities for manipulation, as described in the 2025 CRS report. However, critical challenges persist: auditable AI systems, accountability for algorithmic failures, and transparency in model decision-making remain unresolved, as noted in the 2025 CRS report. Without clear guidelines, the financial sector risks a fragmented approach to managing AI-related risks.

Mitigating the Risks: A Path Forward

Addressing these biases requires a multi-pronged approach. Researchers suggest integrating behavioral economics principles into AI design, such as prompting LLMs to apply the Expected Utility framework to prioritize rational outcomes, as proposed in a 2024 SSRN paper. Additionally, diversifying training data and implementing "bias-aware" prompting strategies could reduce overreliance on recent or authoritative inputs, as noted in the 2024 arXiv preprint.

For investors, due diligence is critical. Platforms leveraging LLMs for portfolio management should disclose their models' limitations and biases, enabling users to contextualize recommendations. Regulators, meanwhile, must prioritize transparency requirements and stress-test AI systems under extreme market scenarios.

Conclusion

The promise of AI in finance is undeniable, but its risks are equally significant. As LLMs increasingly shape investment decisions, their embedded biases threaten to replicate-and even amplify-human errors. Without proactive mitigation strategies and regulatory oversight, the financial sector risks a future where algorithmic decisions are as flawed as those they aim to replace.