Videos
Credendo Rethinks Credit Decisions for a More Volatile World
Insights
- Scalable credit decisioning depends on richer context, combining financials, country risk, sector exposure, and real-world payment behavior.
- As decision platforms mature, the balance between automation and flexibility becomes a strategic choice, not a technical limitation.
- Human judgment remains essential to ensure accountability, adaptability, and trust as credit decisions scale.
In this video, Jean-Paul Steenbeke, Deputy General Manager at Credendo, discusses how credit risk decisions are evolving amid increasing global volatility. He explains how Credendo integrates financial data, country and sector risk, policy parameters, and real-time payment experience into a continuously improving decision platform developed with Infosys. The conversation explores the trade-offs between automation and flexibility, the shift from narrow scoring models to multi-parameter intelligence, and why human judgment remains central even as credit decisioning becomes more automated and scalable.
Jean Paul Steenbeke:
When we look at credit risk decisions we have to define certain steps. So first of all we get the financial data and adapters, we buy that and we get it in via XML files. We also monitor the risk on our debtors on a constant basis. That information then is fed to our scoring model and that leads to financial rating.
We then adapt this rating based on actual country risk, so the latest country risk that we see on a country, and we also take into account sector risk debt.
So it leads to a well-balanced decision, but also a decision that is based on the most recent information that we have. And we add also soft information, such as payment experience, which gives a good indication what an insurance short is about.
Now that is run on a very tailor-made system that we developed, let's say, and put to life four years ago. And that system is now in maintenance together with Infosys. And we try to improve that system constantly together with Infosys. And what we try to do is increase automation, for instance, and get more data in and more, let's say recent information in order to make more relevant decisions.
I must say that automation and tailor-made solutions, they bite each other somewhat. So the more flexibility, the less automation that you have. Now, we have introduced four years ago a new IT system and that already improved the situation quite a lot.
We went from 19 parameters to more than 70 parameters. So that allows a lot of flexibility in the system already. So when you look at credit risk, especially, we look at all the policy parameters. They have an impact on the decision. And then we also look at the financials, at the country risk and at the debtor risk. And together that leads to a very solid decision.
Now there is still the possibility for a person, an underwriter, to make another assessment and to have his knowledge, to have a knowledgeable decision which, let's say, changes the decision of the system. So we need a very robust system but also a very flexible system and that we have today. So this system we developed four years ago and it's in together with Infosys we maintain that.
When we talk about scale, we absolutely need automation and we are working on that on a constant basis to improve and to increase automation on different levels. But it is true that, and that's the same as I said in the beginning, the higher the flexibility and the tailor-made solution, the lower the possibility for automation. So it's also important to define a policy within the parameters of the policy.
