Artificial Intelligence in China’s Banking Sector: Promises, Perils, and Regulation

Amidst the explosive growth of Artificial Intelligence (AI) in China in recent years, Chinese financial market participants, particularly banks, have proactively sought to integrate AI into their operations. Marked by landmark policies, specifically the “Action Plan for Promoting High-Quality Development of Digital Finance” spearheaded by the People’s Bank of China (PBOC), China’s approach to AI in finance is profoundly shaped by two fundamental dimensions. 

First, China’s rich data and advancing financial technology (FinTech) infrastructure establish robust data foundations for AI development. Data, as the core production element of AI technology, directly determines the scope and efficacy of algorithmic models. Bolstered by its extensive population and a series of strategic national policies, China possesses significant data advantages for developing and training AI systems. 

Parallel to this, China’s FinTech sector has demonstrated dynamic growth. At the institutional level, persistent increases in IT investment by financial institutions underscore the strength of this technological transformation. These technological advancements have equipped financial institutions, particularly banks, with the capability to deploy large-scale AI applications more effectively. 

Second, from a regulatory perspective, China, as the world’s second-largest economy, and characterized by a large population and regional disparities, has adopted an innovation-friendly strategy and an adaptive regulatory framework for FinTech as part of its drive towards inclusive and sustainable finance of the nation. While the banking sector plays an essential role in China’s financial system, its current regulatory strategy towards AI focuses on encouraging financial innovation alongside risk management, enabling the rapid deployment and continuous iteration of AI within the industry. This “develop first” approach stands in sharp contrast to the European Union’s (EU) “regulation first” model and the more laissez-faire innovation paradigm in the United States (US). 

While China’s rapid deployment of AI in financial markets has driven significant advancements, it has also introduced substantial risks that require careful regulatory responses. At the technical level, issues such as computational errors and the “black box” opacity of algorithmic models present persistent challenges. At the market level, the widespread use of similar algorithms can increase risk concentration and pro-cyclicality, potentially resulting in model resonance risks. 

A central challenge for both regulators and market participants is effectively managing these risks while fostering technological innovation. Although a growing body of literature and numerous policy reports have examined AI in finance, a distinct research gap remains. There is a scarcity of academic analysis that systematically examines the latest laws and practices in China’s banking sector from a legal perspective. 

My forthcoming paper titled “Artificial Intelligence in China’s Banking Sector: Promises, Perils, and Regulation” seeks to fill the literature gap by addressing three key questions: (1) How is AI being integrated into the banking sector? (2) What are the major risks of AI banking? (3)What roles should regulators play in addressing risks associated with AI adoption in the banking sector? The article first discusses the factors driving the rapid adoption of AI technology by banks in China and clarifies the governance pathway that has emerged, shaped by the country’s distinctive market structure and regulatory approach. It demonstrates that China’s strong policy emphasis on inclusive finance, coupled with the accelerated adoption of digital and AI technologies, offers new avenues for overcoming the Financial Inclusion Trilemma of scale, risk, and profitability in inclusive finance. 

The second part of the paper details the major players and specific application scenarios of AI within the banking industry. It is found that AI offers an alternative solution to address the “Financial Inclusion Trilemma” by enhancing efficiency and expanding access. However, it also introduces complex risks ranging from data integrity issues and model opacity to systemic vulnerabilities. These are risks that traditional regulatory frameworks may be ill-equipped to manage. Consequently, the role of regulation is increasingly critical in safeguarding financial stability without stifling the innovation that fuels economic growth.

Navigating this landscape necessitates a fundamental shift in regulatory capability and strategy. The article suggests a “soft-to-hard” law regulatory evolution roadmap for regulating AI in banking. First, given the high uncertainty and new risks posed by AI, the regulators should adopt a risk-based and adaptive framework for AI in banking. Such an approach can begin with high-level principles or codes issued as soft law, translate these into practical methods and toolkits through industry pilot programmes, handbooks and guidelines, and ultimately evolve into hard law with legal effect (such as Rules or Regulations on AI Risk Management). 

Moreover, traditional static regulation should evolve toward dynamic and technology-enabled oversight. In this context, the deployment of RegTech by financial institutions and SupTech by regulators is a vital component in providing real-time visibility needed to monitor “black box” algorithms. However, technology alone is insufficient if not matched by human capital. Effective regulation requires “putting the human in the loop” (HITL) to ensure accountability where purely technological solutions fail. Also, enhancing regulatory capacity requires a concerted effort to attract multidisciplinary talent. At the same time, it remains important for financial institutions to elevate the sophistication of their internal governance, potentially by empowering roles such as CTOs and dedicated Chief AI Officers. Institutional reforms within regulatory bodies, similar to the MAS’ Fintech & Innovation Group, could also serve as a model for fostering this dual capacity of supervision and innovation facilitation. 

Furthermore, to balance financial stability with innovation in a rapidly evolving banking sector, the regulatory framework should be technology-neutral, proportionate to use-case risk, embedded within existing risk regimes, and refined through close consultation with financial institutions and evidence from practice. Beyond domestic regulatory reforms and efforts, international cooperation remains important for establishing consistent global standards. As illustrated by the strategic partnership between the MAS and UK FCA, such cross-border collaboration may serve as a vital mechanism to help harmonize expectations and facilitate the realization of AI’s transformative potential within a secure banking system.

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Lin Lin is an Associate Professor at NUS Law and an ECGI Research Member.

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