Delays in credit management often arise during the follow-up to credit decisions. With clear policies, smart workflows and AI, standard cases can be processed faster and exceptions can be routed directly to the right person.
AI helps credit risk teams perform credit assessments faster by combining reliable business data, internal information and policy rules. This enables better risk prioritisation, earlier identification of anomalies, and more consistent and well-founded credit decisions.
AI helps real estate organizations analyze lease agreements more efficiently by combining reliable business data with contract information. This makes corporate structures more transparent, enables earlier identification of concentration risks, and supports better-informed real estate decisions.
Many organizations are evolving credit risk management from a reactive process into an integrated, data-driven approach. By combining customer and risk data, they gain better insight into credit risks, including internationally. This makes it possible to identify risks earlier and intervene in a more targeted way. As a result, credit risk management is shifting toward a proactive and strategic approach.
In four steps to a Risk Intelligence Hub: from a single, consistent partner view with Master Data Management, through combining customer and supplier data with Know Your Partner, to linking risk signals to context and group structures. This shifts risk management from reactive to predictive, with greater control over risk, cash flow, and continuity.
Many organizations still focus their credit management on the moment an invoice becomes due. The emphasis is on reminders, follow-up and collections, while the causes of late payments often arise earlier in the process.
Data-driven tenant analysis helps real estate professionals not only assess the current financial situation of tenants but also predict future risks, enabling stable rental income and better-informed investment decisions.
Data-driven risk management helps organizations better manage international risks. Geopolitical tensions, trade barriers, and supply chain disruptions require real-time insight into credit risk and compliance. By identifying and quantifying risks in a timely manner, companies strengthen their resilience and protect their continuity.
A low DSO doesn’t automatically mean your cash flow is under control. Averages hide structural issues like disputes, poor master data, late invoicing, and risky payment agreements. In this article, you’ll learn about the 12 silent and active DSO killers that undermine your working capital and how to tackle them. By focusing on root causes, turnaround times, and risk across the entire Order-to-Cash chain, you can lower DSO structurally, without calling more aggressively.
Many organizations manage Marketing, Sales, and Finance separately, while customers experience them as a whole. These internal silos cause friction, delays, and missed opportunities. By restructuring the organization from the customer's perspective—i.e., aligning processes with the customer experience—you can improve conversion, customer experience, risk management, and customer value. Successful organizations don't look at departments, but at the customer journey as a whole.
Credit management is changing rapidly: from collecting invoices to strategic customer management. Marverick van de Beeten (MaxCredible) explains how data, AI, and human empathy work together to ensure predictable cash flow and stronger customer relationships. Discover how technology is transforming the profession.
Successful organizations no longer make decisions based on gut feeling, but on data. With real-time insights and automated processes, trade credit risk management becomes faster, smarter, and more consistent. Discover how integrated decision-making in CRM and ERP systems reduces risks and increases growth opportunities.
