In the race to digitize credit risk assessment, one mistake keeps tripping businesses up: chasing shiny new tools before fixing their data foundations. The truth is: no technology can outperform poor data. And when operating across borders, that failure is magnified.

For CFOs, the stakes are high. They’re tasked with managing bad debt, driving cost savings, optimizing customer experience, and preventing fraud, all while credit risk grows more complex.

According to Dun & Bradstreet’s latest Global Business Optimism Insights Report, financial confidence fell 2.3% globally in Q4, driven by weaker demand and regulatory uncertainty. This signals growing caution among finance leaders, reinforcing the need for smarter, data-driven credit risk strategies.

So, how do CFOs move from ambition to action? The path begins with a 'data-first' approach to credit risk management.

Digital innovation is more than just risk control; it's a powerful business enabler. Leaders need to define what success looks like and ensure it can be tracked. That means asking: What are my current credit risk capabilities? What do I want them to be? What are the core drivers behind the project?

Improving cash flow, preventing fraud, and reducing Days Sales Outstanding are measurable outcomes that matter. Key Performance Indicators can cover anything from bad debt and data quality to customer satisfaction and process efficiency. Whatever the metrics, they should be revisited regularly to ensure the sustained value.

Digital transformation starts with data hygiene, and that challenge multiplies across borders. For global CFOs, the data needed to accurately assess international credit risk is often messy and inconsistent, varying in format, language and convention. This chaos renders it unusable for a centralized, intelligent risk engine, leading to fragmented insights and poor decisions.

To fix it, CFOs must audit for financial inconsistencies and cleanse historical records; enforce data quality at the source to prevent future problems; and standardize formats while establishing a universal, common data taxonomy across all regions and subsidiaries. This turns fragmented local data into consistent, usable, global credit risk intelligence.

Next, enrich and automate this standardized data using third-party sources, and govern it carefully for compliance across all international jurisdictions. Without this foundation, even the most advanced AI and machine learning tools will fail to deliver consistent, predictable results.

Transformation is a chance to rethink credit review workflows — not replicate them. This requires a willingness to evaluate and redesign processes addressing deep-seated issues like poor team coordination (e.g., between credit, finance and customer service). The goal? Leaner, smarter processes that align with modern systems and customer needs.

To gain a truly global view of credit risk, CFOs must eliminate silos by connecting data across their disparate systems, creating a unified and consistent view of risk. This consolidates risk-related information into a single source of truth, regardless of its country of origin. Running systems in parallel can help map existing decision-making and effectively tune new credit policies before fully going live.

Time, people and training

New software may roll out fast, but people need time to adapt. Success depends on planning for what happens before, during and after implementation.

Achieving stakeholder buy-in is crucial, which means defining clear roles, managing expectations, and empowering teams to lead improvements. When stakeholders can link credit KPIs to wider business goals, they’re more likely to embrace new tools.

Don’t skimp on training. Even the most effective tools can stall without ongoing support. Training must be comprehensive, continuous and focused on how new tools turn data into smarter credit decisions.

Consistent, structured, and, crucially, globally standardized data is the core of successful credit risk transformation. Without it, even the most advanced AI or analytics tools become unreliable.

Poor data hygiene and a lack of standardization across international operations lead directly to bad decisions, costly compliance issues, and wasted investment. The journey to digital maturity in finance is not about buying the fastest engine; it’s about building a consistent roadmap and ensuring the quality of the fuel that drives it.