Fighting Greenwashing Fraud Using Data Analytics

In a high-profile case of greenwashing fraud, a well-known luxury fashion house was at risk of exposure, like many others in the industry, for exploitative and unsustainable labor practices. Misconduct among several suppliers was uncovered through sophisticated data analysis that revealed unusual nighttime electricity usage, suggesting that factories were operating illegally around the clock.

The case drew widespread attention, not only because of the brand involved, but because it highlighted the complex interplay between internal audit, data analysis, and supply chain risk management. Importantly, it underscored the critical need for organizations to rigorously audit suppliers and third parties within their value chain, rather than simply taking them at their word.

While greenwashing has long posed a significant risk, the issue has become even more pressing amid increasingly fervent backlash against environmental, social, and governance (ESG) topics, which has pushed sustainability control frameworks down the corporate agenda. As the complexity and scale of greenwashing fraud continues to evolve, data analytics is emerging as an increasingly essential tool in preventing and mitigating fraud risk before it escalates.

Risk Considerations

Reputational threat is by far the number one risk associated with greenwashing, particularly for organizations in consumer-centric sectors such as lifestyle, fashion, and packaged goods. However, what many fail to recognize is just how far greenwashing risk extends beyond surface-level public perception.

Reputational damage often leads to financial harm, which, in turn, can trigger litigation risk. Take, for example, a company whose stock price plummets following a headline-grabbing greenwashing scandal. The fallout is a chain reaction in which the company’s broader financial health and shareholder returns are compromised. In regions with more mature sustainability laws, regulators may also intervene by restricting the company's access to key capital market transactions, further jeopardizing its financial stability.

Detection Difficulty

Why is greenwashing fraud often more challenging to detect than traditional cyber or financial fraud? Much of it comes down to scope. When investigating greenwashing fraud, the value chain becomes significantly broader — extending beyond the organization itself to suppliers, clients, and third parties involved across the life cycle of the product or service.

The motivations behind greenwashing fraud are also often more complex. It is rarely driven solely by financial gain, and greenwashing can be the result of unintentional mistakes not detected by internal controls. In some cases, it stems from a lack of education or misunderstanding of sustainability practices and reporting; in others, from cultural misalignment in how different parties view environmental concerns, or from attempts to save time and resources by bypassing extensive reporting or control processes.

Greenwashing fraud is also rarely the result of a single individual’s actions. More often it is the culmination of miscommunication or misinformation passed down through multiple stakeholders within an organization, enabled by weak internal controls.

Importantly, greenwashing fraud isn’t just about making false statements. It also encompasses omitting critical information or failing to disclose important data that should reasonably be considered. Ultimately, it is as much about what organizations do not say as what they do.

The Role of Internal Audit

A common misconception is that internal audit primarily exists to investigate wrongdoing after it occurs. In reality, the value of internal audit begins much earlier — within the control environment.

As sustainability regulation continues to evolve across jurisdictions, internal audit serves as a critical line of defense, helping organizations assess whether the right processes and governance frameworks are in place to manage misinformation and mitigate fraud risk from the outset.

This includes identifying gaps in internal controls, weaknesses in oversight, and potential opportunities for employees or third parties to bypass procedures. For example, internal audit plays an important role in assessing whether appropriate sustainability training exists for employees — and challenging whether current trainings are effective — as well as ensuring effective communication processes exist to govern sustainability reporting. 

The Growing Importance of Data Analytics

Data analytics is becoming an increasingly important tool to strengthen sustainability assurance and identify potential greenwashing risks. Specifically, analytics can help internal audit teams review, recompute, and challenge sustainability data before it is disclosed externally. In some cases, internal audit can help identify what is not disclosed.

Effective data analytics goes beyond simply processing data — it involves defining the key performance indicators around risk. Intensity ratios, for example, are widely used to benchmark performance against peers within the same sector or entities within the same organization and can help identify unusual trends or inconsistencies. Text-mining and sentiment analysis can also help reveal misleading language, exaggerated claims, or inconsistencies between emissions data and other public sources.

Importantly, data analytics can also help uncover undisclosed information by identifying anomalies, missing evidence, or inconsistencies across data sets.

For internal audit functions, this means that our continued value increasingly depends on our ability to develop strong analytical capabilities — not only to process and validate data, but also to reassess data governance, integrity, and controls. 

Emmanuel Pascal is a presenter at The IIA's 2026 International Conference, which takes place June 22-24 in Singapore.