Why Data Governance Is Now Central to Fraud Prevention

By Patrice Bouexel, GM Europe, Sis ID

For years, fraud prevention has been viewed as the responsibility of treasury, compliance, internal audit or cybersecurity teams. When fraudulent payments occur, organisations typically look at controls, approval processes and employee training to identify what went wrong. Those measures remain important, but they are increasingly addressing the symptom rather than the cause.

The reality is that many fraud risks begin long before a payment is approved. They originate in the quality of the data underpinning financial processes. Supplier records, beneficiary information and payment instructions all form part of the foundation upon which organisations make decisions every day. When that information is inaccurate, incomplete or outdated, vulnerabilities emerge that even the strongest approval processes may struggle to compensate for.

This is why fraud prevention is increasingly becoming a data governance issue.

Most organisations invest significant effort in detecting suspicious activity. They deploy monitoring tools, strengthen approval workflows and train employees to identify red flags. Yet relatively few devote the same attention to the integrity of the information flowing through those systems. The assumption is often that supplier and beneficiary data is accurate enough to support critical business processes. In practice, that assumption is frequently misplaced.

There is no such thing as a perfect supplier database. Every organisation contains records that are outdated, incomplete or incorrect. Bank account details change, businesses restructure, employees make mistakes and information becomes less reliable over time. Without ongoing verification and maintenance, even well-managed databases inevitably deteriorate.

The challenge is that data quality rarely has a single owner. Procurement teams are responsible for supplier relationships. Finance teams process payments. Treasury teams manage cash flows and banking operations. IT teams maintain systems and infrastructure. Compliance teams oversee controls and regulatory obligations. Each function interacts with supplier data, yet responsibility for ensuring its ongoing accuracy is often fragmented across the organisation.

As a result, data quality can become everyone’s responsibility and no one’s responsibility at the same time.

This creates a significant blind spot. When organisations experience fraud attempts, the response is often to strengthen controls around payment authorisation or employee awareness. Those actions are sensible, but they do not address the underlying question of whether the information being used throughout the process can be trusted. If supplier records are inaccurate or outdated, fraud risks increase regardless of how many approval stages are added to the workflow.

The issue extends beyond fraud. Poor-quality supplier data creates operational inefficiencies, payment failures and unnecessary administrative work. Finance teams spend time investigating exceptions, suppliers experience delays and organisations lose confidence in the accuracy of their own records. While these issues may appear operational rather than strategic, they highlight the broader consequences of weak data governance.

One of the most interesting observations from payment verification programmes is that fraud itself represents only a small proportion of the issues identified. Manual mistakes account for a larger share of verification failures than attempted fraud. Most alerts are generated by outdated records, mismatched information or data quality issues that have accumulated over time. This does not make them less important. On the contrary, it demonstrates how much risk can be created simply by relying on information that has never been independently validated.

The growing adoption of automation makes this challenge even more significant. Organisations are increasingly using technology to streamline finance processes, reduce manual workloads and accelerate decision-making. These investments can deliver substantial benefits, but they also increase dependence on the quality of underlying data. Automated systems process information exactly as it exists. If inaccurate supplier information enters the process, technology can help spread the consequences more quickly and more efficiently.

Artificial intelligence is creating a similar dynamic. Much of the discussion around AI has focused on its potential to improve productivity and support decision-making. However, AI systems are fundamentally dependent on the quality of the data they analyse. They can identify patterns and generate insights at scale, but they cannot determine whether inaccurate information is suddenly trustworthy. As organisations increase their use of AI within finance functions, data governance becomes even more critical.

At the same time, fraud itself is becoming more sophisticated. Deepfakes, voice cloning and AI-generated communications are making it increasingly difficult for employees to distinguish legitimate requests from fraudulent ones. As trust-based controls become less reliable, organisations need stronger foundations upon which to make decisions. Verified, accurate and well-governed data provides that foundation.

This is why leading organisations are beginning to view supplier data as a strategic asset rather than an administrative requirement. Data governance is no longer solely about regulatory compliance or record-keeping. It is becoming a core component of risk management, operational resilience and fraud prevention.

The organisations that perform best in this environment are not necessarily those with the most complex fraud detection systems. They are often those with the strongest control over the quality of their information. They understand where supplier data originates, how it is maintained, who is accountable for it and how it is verified over time. They recognise that effective fraud prevention starts long before a payment reaches the approval stage.

Fraud will always require strong controls, oversight and vigilance. However, as finance operations become more automated, interconnected and data-driven, organisations need to broaden their perspective. The question is no longer just whether suspicious activity can be detected. It is whether the information supporting critical financial decisions can be trusted in the first place.

That is ultimately why fraud prevention is becoming a data governance problem. Organisations that continue to focus exclusively on detection may find themselves addressing issues after they emerge. Those that prioritise data integrity, ownership and verification will be better positioned to prevent problems before they occur. In an increasingly complex payment environment, that distinction matters more than ever.