AI and the fundamental problem of reliability

Ask any finance leader whether their supplier database is accurate, and the answer will usually be the same. They may acknowledge the occasional error or outdated record, but most assume the information underpinning their payment processes is broadly reliable. After all, suppliers are being paid, operations are running smoothly, and major issues appear relatively rare.

 

The uncomfortable reality is that there is no such thing as a 100% accurate supplier database. Every organisation contains records that are outdated, incomplete or incorrect. Bank account details change. Businesses restructure. Employees make manual errors. Information that was accurate six months ago may no longer reflect reality today. The question is not whether inaccuracies exist. The question is how many exist and whether organisations know where they are.

 

This matters because inaccurate supplier data creates risks that extend far beyond payment fraud. When organisations discuss supplier verification, the conversation often focuses on preventing criminals from diverting payments. Fraud prevention is undoubtedly important, but it is only one part of the picture. Poor data quality can create operational inefficiencies, payment failures, supplier disputes and unnecessary administrative costs long before a fraudster becomes involved.

 

One of the most surprising findings from payment verification programmes is that fraud represents only a small proportion of the issues identified. Manual mistakes account for a larger share of verification failures than attempted fraud. A mistyped account number, an outdated beneficiary record or incomplete supplier information can all trigger problems that require investigation and resolution. In many cases, the issue is not malicious activity at all. It is simply inaccurate information sitting within systems that organisations assumed were reliable.

 

The challenge is that supplier databases are not static assets. They are constantly changing. New suppliers are added, banking details are updated, and records are amended by multiple teams across the organisation. Over time, even well-managed databases begin to deteriorate unless there are processes in place to verify and maintain information. Every change creates an opportunity for errors to enter the system, and those errors rarely correct themselves.

 

For finance teams, the consequences can be significant. Failed payments create delays, increase workloads and consume resources that could be better spent elsewhere. Treasury teams may need to investigate exceptions, suppliers may need to be contacted, and payments may need to be reissued. While each incident may appear minor in isolation, the cumulative impact can be substantial, particularly for organisations processing large volumes of transactions.

 

There is also a broader resilience issue at play. Business leaders increasingly rely on data to make decisions about cash flow, supplier relationships and operational performance. When the underlying data cannot be trusted, the quality of those decisions inevitably suffers. Organisations spend significant amounts of money investing in technology, automation and analytics, yet many continue to overlook the quality of the information feeding those systems.

 

This challenge is becoming even more important as businesses embrace automation. Historically, manual processes gave employees opportunities to identify inconsistencies before payments were processed. Automated workflows can deliver enormous efficiency benefits, but they also increase dependence on accurate data. If incorrect information enters an automated process, errors can be replicated quickly and at scale. Automation does not eliminate data quality issues; it often amplifies them.

 

The same principle applies to artificial intelligence. Much has been written about AI transforming finance functions, but AI systems are only as effective as the data they are given. Poor-quality supplier information does not become more reliable simply because it is analysed by an advanced algorithm. In fact, inaccurate data can undermine the value of otherwise sophisticated technology investments.

 

The good news is that data quality improves when organisations treat verification as an ongoing process rather than a one-off exercise. One of the most valuable outcomes of supplier verification programmes is that databases become more accurate over time. Errors are identified earlier, outdated records are corrected and confidence in the underlying information increases. Verification is not simply about preventing bad payments; it is about continuously improving the quality of the data supporting critical business processes.

 

Too many organisations view supplier information as an administrative necessity rather than a strategic asset. Yet supplier and beneficiary data sits at the centre of some of the most important financial processes a business performs. When that information is inaccurate, the resulting risks extend well beyond fraud. They affect efficiency, resilience, decision-making and operational performance.

 

The assumption that supplier data is largely correct is understandable. It is also increasingly difficult to justify. In a world where businesses are becoming more automated, more interconnected and more dependent on data, verification should not be viewed as an optional control. It should be recognised as a fundamental part of maintaining trust in the information that organisations rely on every day. 

 

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Patrice Bouexel is GM Europe at Sis ID

 

Main image courtesy of iStockPhoto.com and tadamichi