Central Bank Data Governance Takes Action: Multiple Banks Fined for Financial Statistics Violations Since the Start of the Year, Regulatory Penetrating Monitoring Upgraded

AI · Banking and Financial Statistics Violations Frequently Occur: What’s the Root Cause?

By Reporter Liu Jakuai | Edited by Huang Bowen

Since the beginning of 2026, several banking institutions have received penalties from the People’s Bank of China for violating financial statistical management regulations.

According to Daily Economic News, on March 3 alone, the Guangxi Zhuang Autonomous Region Branch of the Agricultural Development Bank of China was warned and fined 624,700 yuan for “violating financial statistical regulations”; the Shaanxi Province Branch of the Agricultural Bank of China was fined 380,000 yuan for similar violations.

Looking back to February this year, institutions such as Tianjin Binhai Yangzi Village Bank, Yingtan Rural Commercial Bank, and the Xinyu City Branch of Postal Savings Bank were also fined for violations of financial statistical regulations, with fines ranging from tens of thousands to over a million yuan.

This concentrated enforcement effort targeting the authenticity, accuracy, and timeliness of financial statistical data aligns with the core task outlined at the 2026 central bank work conference: “Research and establish a financial statistical system and standards that match the modern central banking system.”

Sources close to regulators told reporters that when banks submit regulatory reports like 1104 and EAST, they often require extensive manual data extraction, transformation, supplementation, and cross-verification. As regulatory requirements for granularity, transparency, and timeliness of data reporting increase, traditional management models are no longer sufficient. Some fines cite violations of both “financial statistical regulations” and “financial technology management regulations,” exposing systemic deficiencies in underlying capabilities such as system architecture and data quality control.

Screenshot source: People’s Bank of China website

Penalty Insights: All Types of Institutions Under Scrutiny, Statistical Compliance Becomes a “High-Frequency Red Line” for Supervision

Recent administrative penalty disclosures by the central bank clearly show that violations of financial statistics have become a common compliance challenge across the banking industry. Cases involve policy banks, large state-owned commercial banks, joint-stock banks, city commercial banks, rural commercial banks, and village banks. This includes systemically important institutions like China Postal Savings Bank and Agricultural Bank of China, as well as smaller legal entities like Tianjin Binhai Yangzi Village Bank, indicating widespread issues.

According to the 2002 “Regulations on Financial Statistical Management” issued by the People’s Bank of China, financial statistics refer to the collection, collation, and analysis of data related to various financial activities, covering areas such as monetary statistics, credit income and expenditure, financial supervision, and financial market statistics. Article 4 states that the fundamental task of financial statistical work is “to complete various financial business statistics in a timely, accurate, and comprehensive manner,” providing statistical information and consulting advice for macroeconomic decision-making, financial regulation, and institutional management. Any damage to the authenticity, completeness, or timeliness of statistical data directly impacts the accuracy of financial supervision and macroeconomic decisions.

A senior banking researcher pointed out that, based on the descriptions in public penalty notices, the term “violating financial statistical regulations” is a broad category that may encompass various specific violations. These include common issues like “falsifying or concealing financial statistical data,” as well as “forging or tampering with financial statistics,” “refusing to report or repeatedly submitting late reports,” and unauthorized compilation and release of statistical surveys.

Further analysis suggests that some bank branches, especially at month-end or quarter-end critical assessment points, may manipulate data reporting methods or conduct “funds transfer” operations like “loan to deposit” to meet targets such as loan scale or non-performing loan ratios. These distortions cause reported data to deviate significantly from actual business conditions. While such data falsification can temporarily improve reports, it hampers regulators’ ability to accurately assess regional credit deployment and risk, potentially obstructing macro policy implementation.

Root Cause Analysis: Distorted Incentives and Digital Shortcomings Lead to Data Inaccuracy

Beneath the frequent violations of financial statistics lies a systemic issue stemming from internal governance flaws and external technological deficiencies.

The aforementioned researcher believes that the primary root cause is that some banks have yet to fundamentally shift away from a “scale-oriented” and “point-in-time assessment” growth model. In an environment where net interest margins are narrowing and competition among peers intensifies, indicators such as loan and deposit market share and asset growth remain the main performance benchmarks. This top-down pressure often leads frontline staff to “modify” data to meet targets, even risking systemic data falsification.

He emphasized that when employee compensation and promotion are closely tied to month-end or quarter-end figures, the cost of statistical compliance can be distorted into a “necessary price” for achieving performance, making it difficult to eradicate irregularities.

Second, many banks lack robust data governance systems and advanced financial technology applications, making it difficult to meet increasingly complex, detailed, and real-time regulatory reporting requirements.

He explained that many banks, especially small and medium-sized institutions, have built numerous legacy systems over time, but with inconsistent data standards and poor system interoperability, resulting in serious “data silos.”

“Reporting to regulatory systems like 1104 and EAST often requires extensive manual extraction, transformation, supplementation, and cross-verification. This highly manual process is inefficient and prone to operational errors,” he said. As regulatory demands for data granularity, transparency, and timeliness grow, traditional management models are increasingly inadequate.

He pointed out that the coexistence of violations of “financial statistical regulations” and “financial technology management regulations” in some penalties reveals systemic weaknesses in system architecture and data quality control.

Furthermore, some institutions do not sufficiently prioritize financial statistical work or internal controls. According to the Regulations on Financial Statistical Management, the head office (or headquarters) of each financial institution should establish dedicated statistical departments or roles, with responsible persons accountable for data authenticity. However, in practice, statistical work is often viewed as a supplementary task of “reporting figures,” lacking comprehensive data quality management, cross-departmental validation processes, and internal accountability mechanisms. This makes it difficult to detect and correct data issues early.

Regulatory Evolution and Bank Responses: Technology-Enabled Penetrative Monitoring

In response to these systemic issues, regulatory approaches are shifting from individual penalties to building long-term mechanisms and upgrading technological standards.

The reporter noted that the 2026 central bank work conference listed “enhancing financial management and service capabilities” as one of the top seven priorities, explicitly emphasizing ongoing efforts in financial “five big articles” and key areas like debt monitoring of financing platforms.

The researcher predicts that, based on current penalty trends, future regulation will focus more on “building error-resistant mechanisms” rather than merely “detecting and correcting data errors.” This means greater attention to top-level data governance, robust system architecture, and full automation of data generation to reporting processes.

“Data sharing and penetrative monitoring driven by the central bank and other departments must rely on unified data standards and powerful technological platforms to effectively link data across institutions and markets for risk insights,” he said. “As standards like ‘One-Form Pass’ and technological inspection methods deepen, relying on manual intervention and post-hoc corrections will become increasingly unsustainable. Building embedded, intelligent, proactive data quality control systems is now an inevitable necessity for survival and growth.”

For banks, adapting to this new regulatory environment requires profound self-reform.

He recommended three key measures:

First, reform internal performance evaluation and resource allocation mechanisms to reduce dependence on superficial indicators like scale at specific points, shifting toward comprehensive assessments focused on business substance, customer value creation, and risk-adjusted returns—eliminating motivation for data falsification at the source.

Second, elevate data governance and fintech development to strategic levels across the bank, increasing resource investment, promoting core system integration and data platform construction, and realizing full-process automation, standardization, and traceability from business source to reporting output—fundamentally enhancing data production capabilities.

Third, cultivate a compliance culture across the organization that regards “data as an asset” and “truth as the bottom line,” treating data authenticity as an inviolable red line, and establishing internal accountability mechanisms to ensure compliance requirements penetrate every business process and position.