Fraud Risk in ESG Reporting: New Control Obligations for Finance Teams

The demands on ESG reporting are increasing. For finance teams, this means an additional task alongside the regular financial close. Data on environmental, social, and governance (ESG) issues are no longer an optional supplement — they are becoming a central component of corporate reporting. With this growing importance, the ESG reporting fraud risk also increases. The data must be treated with the same care as financial key figures. A study of 22 jurisdictions shows a global saturation in ESG disclosure. The average rate rose from 89.2% to 97.1% between 2019 and 2024 (Delgado Sanchez 2027). This convergence of standards makes the data comparable and therefore business-critical. The responsibility for its accuracy increasingly lies with the finance team.

From a ‘Soft’ Factor to a Hard Asset

For a long time, ESG metrics were considered ‘soft’ information. Their value was difficult to quantify. This perception has fundamentally changed. Sustainability practices have a measurable impact on a company's financial performance. A study of pharmaceutical companies found that ‘Green Accounting’ has a positive and statistically significant impact on financial performance (Jurnal Literasi Akuntansi 2021). Investors, lenders, and insurers use ESG data to assess risks and make investment decisions. A good ESG rating can facilitate access to capital and reduce the cost of capital.

ESG data thus becomes a corporate asset. Like any other asset, this data must be protected from loss, falsification, and fraud. Manipulated ESG figures can lead to incorrect valuations, reputational damage, and legal consequences. The integrity of ESG reporting is therefore no longer a mere communication task. It is a core task of financial leadership and control.

The Limits of Manual Controls

In many companies, the process for collecting ESG data is still not very standardised. The information comes from various departments and sources. It is often compiled manually in Excel spreadsheets. Such manual processes are prone to error and difficult to trace. There is no clear audit trail showing where a figure comes from and who changed it when. The chain from raw data to the final report is broken in many places.

These manual systems do not offer sufficient security. Deliberate or unintentional manipulation of data is very difficult to detect. Plausibility checks, if they happen at all, are only carried out on a random basis. With the growing volume and complexity of ESG data, manual controls are reaching their limits. The risk of false or embellished figures remaining undetected is high. Such an approach jeopardises the value of ESG data and the trust of stakeholders.

Ensuring Integrity Through Digital Processes

Ensuring data quality begins with the digitalisation of processes. A digital transformation improves the quality of accounting information. It increases transparency, reduces information asymmetries, and limits opportunistic management behaviour (Accounting, Finance and Computational Intelligence 2020). The same principles apply to ESG data. A structured, digital process creates a reliable data foundation.

A study was able to demonstrate a positive and statistically significant effect of digital transformation on the quality of accounting information (Accounting, Finance and Computational Intelligence 2020). An end-to-end digital process for ESG data ensures that all steps are documented and traceable. Data is no longer sent by email or copied manually. Instead, it flows into a central system via defined interfaces. This creates the prerequisite for effective controls and closes gaps in the process chain.

Automatically Detecting Anomalies with Algorithms

A robust process alone is not enough to reliably detect fraud attempts. This is where automated analysis methods, which have already proven their worth in financial reporting, come into play. Anomaly detection algorithms can identify patterns in large datasets that would be invisible to a human. A study shows that the use of such algorithms significantly improves the detection of fraudulent financial statements (Journal of Management and Business Solutions 2018).

These techniques can be applied directly to ESG data. For example, an algorithm can compare reported CO2 emissions with production volume and raise an alarm in case of unexplained discrepancies. It can also check the consistency of data across different reporting periods. Machine learning models significantly outperform traditional statistical methods in identifying suspicious patterns (Journal of Management and Business Solutions 2018). In particular, an Autoencoder Neural Network showed very high predictive performance in fraud detection in one study, with an accuracy of 0.93 and an AUC value of 0.96. Such systems learn continuously and adapt to new fraud patterns. They represent an important supplement to internal control systems.

Concrete Steps for Your Finance Team

Securing ESG reporting is a process. You can start laying the groundwork this month. The following steps do not require new tools, but rather a systematic examination of existing procedures.

• Document the data flows: Create an overview of which ESG data is collected. Record which source systems it comes from, which individuals are involved, and where manual transfers occur.
• Identify the key metrics: Focus on the ESG metrics that are material to your company. These are typically those reported to investors or subject to legal requirements.
• Carry out plausibility checks: Compare the current values of the key metrics with those of the previous month and the previous year. Investigate large or unexpected deviations. This is a simple form of anomaly detection.
• Define responsibilities: Clarify within the finance team who bears the final responsibility for the accuracy and completeness of the ESG data. This governance issue is crucial for embedding the controls.

These measures form the basis for a robust internal control system for ESG data. They create transparency about the risks and enable you to initiate targeted improvements.

Microsoft and Excel are registered trademarks of the Microsoft Corporation.