WFY’26: Why poor data can undermine factoring, SCF and AI

Data quality is becoming a critical issue for factoring and supply chain finance providers as automation and artificial intelligence take a larger role in credit decisions, risk management and operational processes. In this specialist article, Raško Perić, Marketing Manager at efcom GmbH, examines why the quality of underlying information can directly affect risk, profitability, regulatory compliance and the reliability of increasingly automated financing systems.

The article included in the World Factoring Yearbook 2026 (WFY’26) considers what good data quality actually means, from completeness and accuracy to consistency and traceability. It also explores the case for treating data as a strategic asset, establishing stronger data governance and building a data-first culture before financial institutions increase their reliance on AI-driven decision engines.

Below is an excerpt from his article.

Data quality is not only crucial in factoring. Regardless of the systems or processing procedures involved – whether traditional rule-based or AI-driven – those who tolerate poor data quality should not complain about modest results. If we reverse the ‘garbage in, garbage out’ rule to ‘quality in, quality out’, factoring and supply chain finance providers in particular will benefit significantly – in several ways. In this short article, we would like to discuss what is generally meant by ‘data quality’, the advantages of good data quality, what to look out for in your own data management, and why a good database will be of crucial importance, especially in AI-driven analysis.

So far, so good. What advantages does good data quality offer from the perspective of factoring or supply chain finance providers? Specifically, these include improved risk management (lower default risks, better liquidity forecasts), greater operational efficiency (less manual follow-up, better automation) and, last but not least, fewer conflicts with regulatory requirements (compliance, DORA (Digital Operational Resilience Act, etc.). In short, data quality has a direct impact on a company’s own risk, profitability, regulatory security and competitiveness.

In this context, let us now consider the ever-increasing use of AI. Whether data is available and whether it has quality, play an increasingly important role in AI-controlled systems. This is because when AI agents are used, they act largely autonomously on the basis of the data and instructions they receive. In such a scenario, there is deliberately no longer any human control authority to detect errors, inconsistencies or deviations after a decision has been made. Consequently, poor data quality in an agent-based model can even lead to systemic risk, as errors are amplified by machines.

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To read the whole article and 50 other specialist articles and country market reviews, order World Factoring Yearbook 2026 here.