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We Approve Trade Credit Accounts on Gut Feel and Some Never Pay. How Do We Decide Better?

Trade credit decided by instinct lets bad payers in and good ones wait. We build machine learning credit scoring from your own payment history to guide you.

Updated 3 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Trade credit decisions made by instinct, with a quick look at a credit report, tend to be slow for good customers and too generous to risky ones. A model trained on how your own past trade accounts actually paid, combined with bureau data, gives each application a risk score and a suggested limit, so the credit controller spends time on the borderline cases.

The account that never paid

A new trade customer applies for a credit account. They seem fine: a business address, a reasonable first order, a friendly buyer. Someone checks a credit report, sees nothing alarming, and approves a limit. Three months later the account is well overdue, calls go unanswered, and the debt ends up with a collection agency.

At the same time, genuinely good customers wait days for an account to be approved because the credit controller is busy and wants to check references. Some of them go elsewhere. The process manages to be both too slow and not careful enough.

Why credit decisions go wrong

Credit decisions in most wholesale and trade businesses rely on one person's judgement plus a bureau score. Bureau scores are useful but generic: they do not know how businesses like this customer have behaved with you, in your sector, buying your products. The judgement is shaped by the last bad debt, which can make the controller overly cautious with one type of customer and relaxed with another.

Your own ledger holds the evidence that matters most: years of trade accounts, the limits they were given, and how they actually paid. That history rarely gets used, because nobody has joined application details to payment outcomes.

Limits suffer from the same gap. A starting limit is often picked from a short list of round numbers and then only reviewed when a customer asks for more. Accounts that have paid reliably for years stay constrained, while newer accounts that have started paying later keep the limit they were first given.

What instinct-led credit costs

PatternWhat it costs
Risky accounts approvedBad debts, write-offs and collection costs
Good customers kept waitingLost first orders and customers who never come back
Limits set by habitSome customers held back, others given too much rope
Inconsistent decisionsSimilar applications treated differently
Controller timeHours spent on easy cases instead of hard ones

Bad debt is the visible cost. The customers who gave up waiting for approval never appear in any report, which is why the slow side of the process rarely gets fixed.

How we score trade credit applications

  1. We pull the history of trade accounts from your accounting system or ERP, such as Sage, Xero, NetSuite or Dynamics, including application details, limits, invoices and payment dates.
  2. We define what a bad outcome means for you, for example payment beyond a certain number of days late or a write-off, and label past accounts accordingly.
  3. We add bureau data from your credit reference provider where you have it, alongside your own signals such as sector, order pattern, company age and how the application was made.
  4. We train a model that estimates the risk of a bad outcome for a new applicant and suggests a starting limit, and we check it for unfair patterns before it is used.
  5. We put the score into the approval process: low-risk applications can be approved quickly within set limits, high-risk ones are declined or asked for more, and the borderline ones go to the credit controller with the reasons shown.
  6. We also score existing accounts regularly, so changes in payment behaviour are flagged before an account becomes a problem.

The credit controller keeps the final say on anything outside the automatic bands, and every override is recorded so the model can learn from it.

What the credit team gets

Clear, low-risk applications approved quickly. The controller's time goes on the borderline cases, with a score and the reasons in front of them. Existing accounts that are starting to pay later are flagged early, while there is still time to adjust terms.

Decisions become consistent and explainable, which matters when a customer asks why their limit is what it is.

Sales benefits as well. When good customers get an account quickly, the first order is not lost to a competitor who said yes sooner, and reps stop chasing credit on behalf of their customers.

Is this your situation?

  • Trade credit is approved by one person using experience and a credit report.
  • Bad debts from newer accounts are a recurring cost.
  • Good customers complain about how long account approval takes.
  • Credit limits are set by habit rather than evidence.
  • You have several years of trade accounts with payment history.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

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Is this suitable for a smaller wholesaler?

If you have enough past accounts with known outcomes, yes. With few bad debts to learn from, the model leans more on bureau data and simple rules.

Does this replace our credit reference agency?

No. Bureau data is one of the inputs. The model adds your own payment history on top.

How do you make sure the scoring is fair?

We check how the model treats different groups of applicants, exclude inputs that should not influence credit decisions, and keep reasons visible for every score.

What drives the cost?

The quality of your ledger history, whether bureau data is available through an API, and how the score fits into your approval process.

What do you need from us?

Trade account history with payments, your current credit policy, and access to bureau data if you use it.

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