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

Everyday Economic News Reporter | Liu Jukai Everyday Economic News Editor | Huang Bowen

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

Notably, 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 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 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 framework that matches the modern central banking system.”

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

Screenshot source: People’s Bank of China website

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

Recent administrative penalty disclosures by the People’s Bank of China clearly outline that violations of financial statistics have become a common compliance challenge for banks. 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 such as 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 statistics, financial regulatory statistics, 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 breach of 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 statistical data,” “refusing to report or repeatedly delaying reporting financial statistics,” and unauthorized compilation and release of statistical surveys.

Further analysis suggests that some bank branches, especially at month-end or quarter-end, 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 report appearances, it hampers regulators’ ability to accurately assess regional credit deployment and risk, creating obstacles for macro policy implementation.

Root Cause Diagnosis: Distorted Incentives and Digital Shortcomings Leading to Data Inaccuracy

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

The aforementioned expert 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 market share of deposits and loans, and asset growth rates, remain the “guiding stars” for many banks’ performance evaluations. This top-down pressure can lead 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.

Additionally, many banks, especially small and medium-sized institutions, have built numerous legacy systems, but face issues such as inconsistent data standards and poor system interoperability, resulting in serious “data silos.”

He explained, “When submitting reports like 1104 and EAST, banks often rely heavily on manual data extraction, transformation, supplementation, and cross-checking. This labor-intensive process is inefficient and prone to operational errors.” As regulatory demands for data granularity, transparency, and timeliness increase, traditional management models are increasingly inadequate.

The expert further pointed out that the coexistence of violations related to “financial statistical management” and “financial technology management” in some penalties reveals systemic weaknesses in system architecture and data quality control.

Moreover, some institutions lack strategic emphasis on financial statistical work and internal controls. According to the “Regulations on Financial Statistical Management,” head offices (or headquarters) should establish dedicated statistical departments or roles, with responsible personnel accountable for data authenticity. However, in practice, statistical work is often viewed as a supplementary task of “reporting figures,” without establishing comprehensive data quality control mechanisms, cross-departmental validation processes, or strict internal accountability, making it difficult to detect and correct data issues early.

Regulatory Evolution and Bank Responses: Technology-Enabled Penetrative Monitoring

In response to systemic issues in financial statistics, 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 seven key tasks for the year, emphasizing ongoing efforts in “the five major areas of financial ‘big articles’” and monitoring key fields such as debt in financing platforms.

The expert predicts that, based on current penalty trends, future regulation will undergo a profound shift: from “detecting and correcting data errors” to “building mechanisms that make errors difficult to occur.” This means supervision will focus more on top-level design of data governance, system robustness, and full-process automation from data generation to reporting.

“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, enabling risk insights.” He added, “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 an inevitable choice for survival and growth.”

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

He suggested three key areas for banks to focus on:

First, reform internal performance evaluation and resource allocation mechanisms to reduce dependence on superficial indicators like scale at specific points in time, shifting toward comprehensive assessments emphasizing 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 middle platforms, 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 assets, authenticity as the bottom line,” treating the truthfulness of statistical data as an inviolable red line, and establishing corresponding internal accountability mechanisms to ensure compliance requirements penetrate every business process and position.