In the world of corporate finance, managing credit risk is essential. Many organizations face the risk of customers not paying their invoices, or paying them too late, which can lead to cash flow issues and even losses. A powerful way to better understand and mitigate these risks is by conducting a retro analysis of your existing customer base.
But what exactly does such an analysis involve? And how can you use the insights you gain to improve your screening process? In this blog, I’ll explain how a retro analysis can help you identify risks and learn from your own data to make smarter financial decisions.
What is a retro analysis?
A retro analysis means looking back at your existing customer base and analyzing their payment behavior over a specific period. You examine which customers pay well and which ones consistently pay late, or not at all. It’s a form of data analysis where you don’t just focus on payment behavior, but also on other characteristics of the companies in your customer base.
Which characteristics do you analyze?
The power of a retro analysis lies in combining multiple types of data. Think of:
- Payment behavior: Which customers have paid invoices late in the past? Are there customers who regularly had payment delays? And how large were those delays?
- Sector: In which sectors are the customers who pay poorly? Are there certain industries with higher risks?
- Age of the company: Are younger companies more often problematic than established names? Companies that have just started sometimes have less financial stability.
- Number of employees: Does the size of a company influence payment behavior? Smaller companies often have fewer financial buffers, for example.
- Financial figures: Review annual accounts and other financial data such as solvency, liquidity, profitability, and debt levels.
By linking this data, you get a more complete picture of your customer base and the risks involved.
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What do you learn from these insights?
The biggest advantage of a retro analysis is that you learn which characteristics have predictive value for payment risks within your customer base. This means you don’t have to blindly rely on general assumptions, but instead know exactly which signals you can recognize in advance.
For example, you learn that companies in certain sectors need extra close monitoring, or that you should be cautious when accepting young companies without sufficient financial backing. You also gain a better understanding of what to watch for when screening new customers and reassessing existing ones.
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How do you use these insights for smarter financial screening?
With the knowledge from your retro analysis, you can sharpen your credit policy and screening processes:
- Targeted screening: You develop criteria based on the risk profiles identified in your analysis. For example: for customers from high-risk sectors, you perform a more thorough credit check and request additional collateral.
- Dynamic monitoring: You can periodically reassess existing customers based on their sector, size, and financial figures, allowing you to detect early if the risk is increasing.
- Adjusting payment terms: For customers with higher risk, you can apply stricter payment terms, such as shorter payment deadlines or requiring advance payment.
- Preventive actions: By detecting risks early, you can intervene faster with reminders or payment arrangements before arrears become too large.
Conclusion
A retro analysis of your customer base is a valuable tool for identifying and managing risks. By examining both payment behavior and customer characteristics, you gain insight into which factors contribute to payment problems. With these insights, you can improve your credit policy, make financial screening smarter, and better protect your organization against unforeseen losses. It’s a strategic step to learn from your own data and proactively manage your financial risks.
Want to manage risks? Then start analyzing your customer base today and discover which patterns and signals can help you make better decisions.
