Where delays really arise<\/strong><\/h2>\n\n\n\nThe causes of late payments are more often internal than is assumed. Small deviations in data and processes often have a greater impact than expected.<\/p>\n\n\n\n
Common examples include:<\/p>\n\n\n\n
\n- Invoices without the correct reference or PO number<\/li>\n\n\n\n
- Pricing agreements that do not match contracts<\/li>\n\n\n\n
- Outdated or incomplete customer data<\/li>\n\n\n\n
- Unclear agreements and terms<\/li>\n<\/ul>\n\n\n\n
On their own, these may seem like minor details. In practice, they lead to questions, corrections, and delays in approval and payment. Disputes are therefore rarely incidental, but often form a structural pattern. As Friso van de Beeten, CEO at Maxcredible, points out:\u201cThe most surprising thing is that most of the reasons for late payments lie with you as an organization\u2014not with the customer.\u201d<\/em>.\u201d<\/p>\n\n\n\nThese bottlenecks rarely arise in a single step. They build up throughout the process and often remain invisible until their impact becomes noticeable in your numbers.<\/p>\n\n\n\n
In a recent webinar<\/a> that we organized together with MaxCredible, we explored this in more depth and discussed how these internal causes accumulate in the order-to-cash process and what is needed to gain better control over it.<\/p>\n\n\n\nDriving insight and predictability<\/strong><\/h2>\n\n\n\nIf the cause of delays lies in processes and data, it calls for a different way of working. Credit management shifts from reactive to predictive. Not only following up afterwards, but identifying risks earlier and responding to them.<\/p>\n\n\n\n
With the right data, organizations gain better insight into customer behavior and can work more effectively. This helps with:<\/p>\n\n\n\n
\n- Better customer segmentation<\/li>\n\n\n\n
- Earlier identification of risks<\/li>\n\n\n\n
- More targeted follow-up<\/li>\n<\/ul>\n\n\n\n
This leads to fewer arrears and a more stable cash flow.<\/p>\n\n\n\n
Process and data quality as prerequisites<\/strong><\/h2>\n\n\n\nTo make this possible, both data and processes must be in order.<\/p>\n\n\n\n
Insight starts with the right information. This means that internal data must be supplemented with external insights. Without that context, visibility into changes in creditworthiness and payment behavior is lacking.<\/p>\n\n\n\n
In addition, data-driven working requires a process that supports these insights. Payment delays are rarely caused by a single error. More often, they result from a combination of inefficiencies, such as incomplete invoice information, incorrect contact persons, and systems that do not integrate properly. Combined with limited alignment between sales, finance, and operations, this leads to friction in the order-to-cash process.<\/p>\n\n\n\n
The pressure to get this in order is increasing. With the introduction of e-invoicing, errors become visible more quickly and the room for manual corrections disappears. At the same time, AI makes it possible to better identify risks and prioritize actions, provided the underlying data is in order. Data quality is therefore no longer an optimization, but a prerequisite.<\/p>\n\n\n\n
Interesting read:\u00a0<\/strong>Everyone wants to do something with AI, but is your organization ready for it?<\/a><\/p>\n\n\n\nInsight as a starting point for improvement<\/strong><\/h2>\n\n\n\nImprovement starts with insight into where the process slows down. By identifying this clearly, you can make targeted adjustments.<\/p>\n\n\n\n
In practice, this means:<\/p>\n\n\n\n
\n- Mapping bottlenecks<\/li>\n\n\n\n
- Segmenting customers based on behavior and risk<\/li>\n\n\n\n
- Combining internal and external data<\/li>\n<\/ul>\n\n\n\n
Organizations that take this step reduce their DSO and gain more control over their cash flow and predictability.<\/p>\n\n\n\n
On their own, these may seem like minor details. In practice, they lead to questions, corrections, and delays in approval and payment. Disputes are therefore rarely incidental, but often form a structural pattern. As Friso van de Beeten, CEO at Maxcredible, points out:\u201cThe most surprising thing is that most of the reasons for late payments lie with you as an organization\u2014not with the customer.\u201d<\/em>.\u201d<\/p>\n\n\n\n These bottlenecks rarely arise in a single step. They build up throughout the process and often remain invisible until their impact becomes noticeable in your numbers.<\/p>\n\n\n\n In a recent webinar<\/a> that we organized together with MaxCredible, we explored this in more depth and discussed how these internal causes accumulate in the order-to-cash process and what is needed to gain better control over it.<\/p>\n\n\n\n If the cause of delays lies in processes and data, it calls for a different way of working. Credit management shifts from reactive to predictive. Not only following up afterwards, but identifying risks earlier and responding to them.<\/p>\n\n\n\n With the right data, organizations gain better insight into customer behavior and can work more effectively. This helps with:<\/p>\n\n\n\n This leads to fewer arrears and a more stable cash flow.<\/p>\n\n\n\n To make this possible, both data and processes must be in order.<\/p>\n\n\n\n Insight starts with the right information. This means that internal data must be supplemented with external insights. Without that context, visibility into changes in creditworthiness and payment behavior is lacking.<\/p>\n\n\n\n In addition, data-driven working requires a process that supports these insights. Payment delays are rarely caused by a single error. More often, they result from a combination of inefficiencies, such as incomplete invoice information, incorrect contact persons, and systems that do not integrate properly. Combined with limited alignment between sales, finance, and operations, this leads to friction in the order-to-cash process.<\/p>\n\n\n\n The pressure to get this in order is increasing. With the introduction of e-invoicing, errors become visible more quickly and the room for manual corrections disappears. At the same time, AI makes it possible to better identify risks and prioritize actions, provided the underlying data is in order. Data quality is therefore no longer an optimization, but a prerequisite.<\/p>\n\n\n\n Interesting read:\u00a0<\/strong>Everyone wants to do something with AI, but is your organization ready for it?<\/a><\/p>\n\n\n\n Improvement starts with insight into where the process slows down. By identifying this clearly, you can make targeted adjustments.<\/p>\n\n\n\n In practice, this means:<\/p>\n\n\n\n Organizations that take this step reduce their DSO and gain more control over their cash flow and predictability.<\/p>\n\n\n\nDriving insight and predictability<\/strong><\/h2>\n\n\n\n
\n
Process and data quality as prerequisites<\/strong><\/h2>\n\n\n\n
Insight as a starting point for improvement<\/strong><\/h2>\n\n\n\n
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