B2B Payment Default: Automated Credit Check at Checkout

A bad debt hits harder than the raw sum suggests. If an invoice of 5,000 euros goes unpaid, a business on a ten percent margin has to generate fifty times that in additional revenue just to make up for the loss. That is exactly why the current trend is a commercial issue and not merely a legal one. The number of filed regular insolvencies was recently well above the previous year (Destatis), and credit insurers' forecasts also point to further rising corporate insolvencies (Allianz Trade). For a shop that offers purchase on account this means the environment in which it pays in advance has become more uncertain - and the blanket approval of purchase on account is riskier than ever.

There is also a chain effect. When a larger buyer becomes insolvent, it often drags its suppliers down because their open receivables default. The damages to creditors from corporate insolvencies in Germany add up year after year to a double-digit billion-euro figure (Creditreform). A single shop cannot change this macro weather, but it can control to whom it grants advance performance under these conditions. The decisive question is no longer whether to offer purchase on account, but to whom and on what terms. Anyone making that decision manually at every checkout is too slow; anyone not making it at all pays the price. The solution is to make it automatically and data-based in the checkout.

Technically the check runs in the background without holding up the customer. As soon as a payment method carrying default risk comes into question at checkout, the shop queries a credit agency for a score on the company. The agency returns an assessment within seconds and often a recommended credit limit. A rules engine maps this score to a risk class and shows only the payment methods approved for that class. Ideally the customer notices nothing of this, except that with good standing they are offered purchase on account and with high risk only prepayment. The whole process takes barely longer than the rest of the page build.

So that the query does not cost money on every click, it is triggered deliberately - for example only when a risky payment method is selected, above a certain cart value or for new customers without a history. For existing customers in good standing, a score once obtained can be reused for a while and refreshed at sensible intervals rather than bought anew with every order. This control also decides the economics: a check that only kicks in where risk actually exists keeps costs low and friction for good customers minimal. The prerequisite is a clean technical connection, as described in our article on interface architecture for B2B shops.

A robust judgment does not come from a single figure but from several sources. The credit agency score is the basis, yet it gains meaning when your own data is added: the customer's payment history from the ERP, open items, past dunning runs and formal signals from registration. The connection runs via the agency's API, which takes company name and address and returns a score plus a recommended limit. It is crucial that this query is reliable, fast and fault-tolerant - if the agency is ever unavailable, the shop should not block but fall back to a safe option such as prepayment.

For your own history to feed in at all, it has to be kept cleanly in the ERP. Open invoices, incoming payments and dunning status belong in one place from which the shop can retrieve them. How accounting is connected for this is covered in our article on the DATEV interface in e-commerce. And already at the registration of a business customer, first signals can be checked and data captured in a structured way, as our article on business customer onboarding and registration shows - the earlier the data quality is right, the more reliable the later credit decision turns out.

A customer checked once is not checked forever. Especially in ongoing business relationships the risk grows insidiously: the customer orders regularly, their open balance grows, and at some point more is at stake than their standing can bear. That is why a credit check belongs together with a credit limit per customer that the open balance must not exceed, plus ongoing monitoring of that balance. When a customer approaches their limit, an early warning is better than a hard block at the checkout. That keeps purchase on account convenient for reliable customers while the shop keeps the total exposure per customer in view.

The start succeeds not through technology but through your own figures. Those who know where receivables have defaulted in recent years and what patterns lay behind them can target the check where it delivers the greatest benefit. Then follows the choice of credit agency, the definition of risk classes and the step-by-step activation - first observing, then binding. This creates a system that fits your own customer structure rather than imposing a template.

Those who think of the credit check as a fixed part of the checkout - with an agency score, risk classes, credit limits and a clean ERP connection - can open purchase on account generously for solvent customers and secure the risky cases without giving away revenue. Which expansion stage makes sense for your shop depends on assortment, customer structure and risk appetite; comparable project approaches show the range, and we are happy to discuss the concrete implementation in direct contact.