Social trading democratizes financial markets by allowing individuals to follow and replicate experienced investors through online platforms, fostering collaboration and shared insights. Artificial intelligence (AI) plays a pivotal role in this process by automating data analysis, filtering noise, and providing personalized portfolio recommendations, thereby empowering novice traders with actionable strategies. This integration enhances decision-making and risk management, accelerating innovation in collaborative investment ecosystems. However, it also introduces systemic risks that require next-generation regulation to ensure market integrity and investor protection. In the context of AI-driven social trading, systemic risk refers to the amplification of market instability through interconnected algorithmic decisions and behavioral feedback loops. When multiple platforms or users rely on similar AI models, correlated actions can trigger rapid market contagion and liquidity shocks ([5]).

Unlike earlier financial technologies, AI’s complex algorithms and the dynamics of user interaction on social trading platforms create novel vulnerabilities, including algorithmic opacity, potential market manipulation, and the rapid propagation of financial shocks. Current regulatory frameworks struggle to address these challenges, often focusing on transparency and accountability while neglecting broader systemic risks.

This paper contributes to the literature by examining how AI-driven social trading platforms harness collective intelligence to improve investment decisions and risk management, while simultaneously amplifying systemic vulnerabilities. It emphasizes the urgent need for adaptive regulatory frameworks capable of balancing innovation with financial stability and consumer protection in the age of AI ([12]).

Social trading platforms now use AI-powered analytics to help users assess trader performance and spot new trends in group trading activity ([5]). These tools give users clearer information and better support for making decisions, which helps build trust and encourages more people to join social trading platforms ([11]). AI-based sentiment analysis and prediction models allow platforms to check trader credibility and better forecast market changes, making shared strategies more trustworthy ([4]). Machine learning also helps spot unusual trading patterns automatically, improving risk controls and letting platforms react quickly to potential issues ([6]). AI also lets platforms track group trading sentiment in real time, adjust risk settings on the fly, and improve stability during sudden market shifts.

Popular social trading platforms like eToro seem to use AI to analyze trader performance and sentiment, helping users choose who to copy and manage risk more personally. They rank strategies with AI and detect suspicious activity to protect users from scams or poor traders. Machine learning also helps optimize portfolios in real time for better risk control. While these platforms all seem to use AI for decision support and risk management, each has a different focus—eToro emphasizes user experience and social features, others focus on strategy reliability or portfolio management—showing the variety of approaches in social trading. For instance, during periods of high volatility, sentiment-based AI models on social trading platforms may simultaneously flag similar trading opportunities, prompting correlated trades that exacerbate market swings, a phenomenon consistent with the herding effects identified in recent analyses of AI-driven financial markets ([5]).

AI-driven social trading platforms increasingly employ deep learning models to process vast behavioral and transactional data, enabling more adaptive risk management and real-time fraud detection capabilities ([7]). However, the integration of such complex AI mechanisms introduces new layers of systemic risk, including model opacity and potential algorithmic herding effects that may amplify market volatility ([5]).

Current EU regulatory frameworks exhibit significant gaps in addressing the unique challenges posed by AI-driven social trading, notably in algorithmic transparency, accountability, and explainability. Existing regulations often lack specificity regarding AI model governance, resulting in insufficient oversight of complex machine learning systems and their potential for systemic risk amplification. Furthermore, data privacy provisions do not fully encompass the extensive behavioral data utilized by AI in social trading, raising concerns about user consent and data security. These shortcomings underscore the urgent need for adaptive, technology-specific regulations that balance innovation with robust consumer protection and systemic stability ([6]; [3]).

AI enhances prediction accuracy and trading efficiency on social trading platforms by reducing human bias and improving risk management, which can stabilize returns. However, excessive reliance on algorithms may lead to herding behavior, market inefficiencies, and systemic vulnerabilities. Challenges such as data quality issues, algorithmic opacity, and ethical concerns necessitate robust governance, transparency, and oversight to ensure AI positively impacts returns and systemic stability in social trading environments.

As AI increasingly permeates social trading platforms, there is a pressing need for next-generation regulations that address the unique systemic risks posed by algorithmic opacity, data privacy concerns, and herd behavior amplification. Effective governance frameworks must balance fostering innovation with ensuring transparency, accountability, and resilience against cascading market failures. Collaborative efforts between regulators, platform operators, and AI developers are essential to implement adaptive, risk-based oversight mechanisms that can evolve alongside technological advancements and market dynamics. Such proactive regulation will be indispensable for safeguarding market integrity and protecting investors in AI-driven social trading ecosystems ([5]). Supervisory Technology (SupTech) enables regulators to harness AI tools for real-time monitoring of market behavior, anomaly detection, and algorithmic audit trails. Integrating SupTech into oversight mechanisms would allow for dynamic, data-driven supervision of AI-based social trading platforms, enhancing transparency and resilience in fast-evolving digital markets ([8])

Regulations for AI in social trading are evolving, but often lag behind rapid technological advances, primarily addressing transparency, accountability, and bias while overlooking systemic risks and data management challenges.

The EU’s GDPR and forthcoming AI Act impose strict standards on high-risk AI, mandating clear explanations, continuous monitoring, and ethical safeguards to protect investors and market integrity. However, issues like opaque algorithms, inconsistent national rules, and market volatility persist, highlighting the need for flexible, collaborative regulation that balances user protection, systemic stability, and innovation. In addition to the EU’s evolving regulatory landscape, the United States and Asia present distinct yet complementary frameworks addressing AI in social trading. The U.S. emphasizes algorithmic accountability and transparency through initiatives like the Algorithmic Accountability Act, focusing on mitigating biases and ensuring fair market practices, while also grappling with balancing innovation and consumer protection ([12]). Asian regulatory approaches, notably in China, prioritize data governance and state oversight, reflecting concerns over data privacy and systemic stability amid rapid technological adoption ([15]). Harmonizing these diverse regulatory paradigms is critical for fostering global cooperation, ensuring robust governance, and mitigating systemic risks in AI-driven social trading ecosystems.

Table 1summarizes key regulatory differences among the European Union, the United States, and China.

Table 1. Comparative breakdown.

This table points out key differences among EU, USA, and China. It focuses on regulatory framework, challenges, and systemic risk mitigation, which are the core aspects in facing the most relevant challenges of AI in social trading.

This research explores the risks that come with using AI and big data analytics on social trading platforms. Recent data shows that trade copying is 35% faster and market ups and downs increase by 20% during stressful times. These numbers show there is an urgent need for updated rules to keep things clear, fair, and safe. Real-time monitoring and global standards are necessary. While AI and blockchain can help with security and spotting fraud, current rules don’t work well across borders. This makes it important to have systems that can oversee these platforms on a larger scale and keep financial systems stable ([5]; [10]; [13]).

The Basel Committee on Banking Supervision (BCBS) has clear guidelines for managing risks in AI-powered social trading. At first, Basel’s rules were for making banks safer by improving capital and risk controls, but the same ideas, like handling market, operational, and systemic risks, also fit digital trading platforms. While Basel principles were originally designed for banking institutions, their core risk management concepts, such as stress testing and proportional oversight, can be adapted to social trading platforms through algorithmic audits and resilience buffers tailored to platform-specific risks ([1]).

Basel III added tools like the countercyclical capital buffer and liquidity coverage ratio to help banks stay steady during tough times and limit the effects of financial swings. Applying these ideas to social trading could mean stricter algorithms, regular checks of AI models, and clear rules for openness and oversight. Basel’s proportionality principle—adjusting rules based on a platform’s size and importance—prevents smaller companies from being overwhelmed. If banking regulators using Basel rules work together with digital market supervisors, they can take a joint approach that keeps financial systems stable and improves AI-based trading. In the EU, Basel guidelines form the base for managing systemic risk. Although Basel III isn’t directly enforced, its standards are included in EU laws like the Capital Requirements Regulation (CRR, Regulation (EU) No 575/2013) and the Capital Requirements Directive (CRD IV-VI, Directive 2013/36/EU and updates). These laws set standards for capital, liquidity, and leverage to keep markets steady. At the same time, the upcoming EU Artificial Intelligence Act (COM/2021/206 final) will set strict rules for transparency, accountability, and risk management in high-risk AI systems. Together, Basel’s risk controls and the AI Act’s focus on algorithm checks create a strong framework. This helps keep financial systems stable as new risks appear with AI-powered social trading platforms ([12]). Unlike MiFID II, which primarily addresses investor protection and market transparency, next-generation regulation must explicitly target algorithmic accountability, cross-platform systemic risk monitoring, and the use of AI for supervisory oversight ([3]).

This research explores how AI-powered social trading platforms help people trade more efficiently and make markets more liquid. However, these platforms also bring new risks, such as algorithms feeding off each other, groups of users moving the market together, and ways for bad actors to manipulate trades. Making AI systems more transparent and using privacy tools can help users see how the platform works, reduce unfair actions, and protect personal information ([5]).

Tools like RegTech and blockchain help with following rules and catching fraud, but they can also make things more complicated, especially, when mixed with decentralized finance and AI trading bots. The Basel framework, which guides banks on how to stay stable, can offer useful ideas for social trading. For example, Basel III tells banks to keep extra money on hand to handle surprises. Social trading platforms could use similar steps, like testing their algorithms under stress, keeping extra safety funds, and setting fair rules for everyone. Using Basel-style protections would help social trading platforms follow standards similar to traditional banks and support stability in both old and new financial markets ([3]). To ensure trustworthy and resilient AI in social trading, policymakers should pursue adaptive, risk-based regulation. Key policy takeaways include:

Mandatoryalgorithmicaudits to enhance transparency and detect bias.Systemicstabilitystresstests to anticipate and mitigate market shocks.Transnationalregulatorysandboxes for real-time oversight and cross-border coordination.Robustdatagovernanceframeworks to protect privacy and manage behavioral data responsibly.IntegrationofBasel-inspiredprinciples, such as countercyclical stress buffers and resilience requirements, to align AI-driven platforms with established standards of systemic risk management in banking.

By embedding these measures in global AI governance, regulators can balance innovation with investor protection and market stability, ensuring that social trading evolves into a safe and sustainable component of modern financial ecosystems. This paper advances the debate by shifting the focus from algorithmic fairness to systemic resilience, offering a roadmap for sustainable AI integration in global financial ecosystems.