In this guide, we'll break down how the credit decisioning process works, some key things to watch out for, and alternative options like iwocaPay that can make the entire process of obtaining trade credit much easier.
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In the world of business, credit is the lubricant that makes everything run and helps suppliers fuel the orders that retailers keep on the shelves. Most people in business want the best credit terms possible, but have absolutely no idea how lenders really, truly come up with the decision of who to lend to and on what terms. So, how does credit decisioning and business credit evaluation really work?
Credit decisioning is how a business decides whether to offer credit and on what terms, and whether a business gets credit or not. In B2B, there is an ongoing decision-making process as customers are constantly buying things, paying for things, and disputing invoices. Sometimes a business might need more of a certain order or need to change the makeup of their product altogether. These changes will cost money, and if the business is not liquid enough, then some form of trade credit is the only option.
In order to facilitate credit, a lender or business offering it will look at a variety of options, including "credit score”,, which most people who have held a credit card or applied for a mortgage might be familiar with.
A credit score is just one of the inputs involved in the credit decisioning, not the decision itself. For example, a company located in Italy might have a perfect credit score, but due to the fact that it is based in Italy and not the UK, a UK bank might factor that in as a downside when deciding whether or not to offer this business credit. A credit score is a numerical representation of the creditworthiness of a business based on an amalgamation of data. It looks at basics like income, past payments, and how much debt someone already has. The result is a yes, no, or approved with limits, such as a smaller loan or higher interest rate.
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Businesses assess creditworthiness by looking at two factors: will they pay you back, and what's the upside of working with them if their track record is not ideal. i.e., "Will this business pay me back on time, or do I need to head down to Barking and Dagenham with the lads in tow to ask for the money?" As this is a complex decision with many moving parts, businesses typically use a plethora of different data sources, credit decisioning tools, and sometimes an overwhelming amount of compliance and bureaucracy. At the end of the day, the goal is to mitigate the risk as much as possible, and although different businesses use different tactics, the overall strategy says the same. In order to assess creditworthiness, businesses and lenders will blend external signals (filings, bureau data) with internal signals (order patterns, payment behaviour). Businesses can extend credit in line with their risk appetite.
Most B2B credit decisions rely on a combination of customer-provided information, third-party data, internal transaction history, and other metrics. No single source is complete, which is why layering signals leads to more reliable decisions when giving a credit risk assessment. Ones to pay attention to are as follows:
In practice, behavioural and transactional history often becomes more predictive over time than a single snapshot of financial health.
Once the business or lender has as much data as possible, they will use their own process when making their credit decision. Some of the most common ones are listed below, and are usually contingent on the business type and scale when looking at models that fit.
When people imagine the credit risk assessment or credit decisioning process, the image of a boardroom with men and women in professional clothing comes to mind. In reality, it's simply a checklist based on different metrics and data points that point one direction or another when it comes to the actual decision-making process. There are also differences between banks offering trade credit and businesses offering trade credit to their customers. In many cases, the credit decisioning process flow will look like this:
For businesses offering trade credit to customers, credit decisioning starts when a customer asks to buy now and pay later, places a large order, or switches from paying upfront to paying on account. At that point, the business collects basic details about the customer so it can confirm who they are and check outside data sources, like credit files or company records, before deciding whether to approve credit.
For a bank, credit decisioning usually starts when a business applies for something like a credit line, overdraft, or invoice financing. The bank gathers financial statements, bank history, repayment behavior, and external credit data, then applies its own rules and risk models to decide whether to approve, how much credit to offer, and on what terms.
Typical intake fields include:
This step pulls in outside data and internal records, like credit reports, public filings, identity checks, and past payment behavior, to confirm the business is real and assess how risky it might be.
Risk assessment estimates late or non-payment risk and sets appropriate exposure levels.
B2B decisions are often conditional, balancing access to terms with exposure control.
In practice, credit decisions tend to fall into a small number of predictable outcome groups. Many applications are approved outright, a significant share are approved with limits or conditions attached, and a smaller portion require manual review or are declined altogether. The breakdown below reflects a common pattern seen in structured B2B credit decisioning workflows and is intended to illustrate how outcomes are typically distributed rather than represent a specific dataset.
Monitoring prevents exposure from growing unnoticed and supports early intervention.
When triggers fire, adjust limits or terms quickly. Conditions only work if they are simple to enforce.
Automation improves consistency and helps businesses scale trade credit without scaling manual effort at the same rate.
Most things are at least semi-automated these days, and credit decisioning is moving in the same direction. Manual processes rely heavily on human review, i.e., "the boardroom of decision makers, " which in reality is nearly impossible without 10,000 extra employees to manually review everything. Technology has come far enough that the majority of the small decisions that make up the overall credit decisioning process are not automated.
The comparison below uses illustrative figures to show the typical direction and scale of these differences.
Most problems show up when decisions are made with thin data or no clear follow-up. For example, approving a new customer based on a name and email alone can feel fine at first, until invoices go unpaid and no one knows who owns the risk. Credit works best when the risk is clear, owned, and actively watched.
Small businesses often have limited public data or records that lag reality. A new wholesaler might look risky on paper simply because last year’s filings are missing, even though orders are growing fast. Pulling in multiple signals and starting with a modest credit limit helps avoid a hard yes or no based on incomplete information.
Imagine a policy that rejects any customer without two years of accounts. Sales teams will rue the day that policy was implemented. Flip it the other way and approve everyone, and overdue balances spike within months. Tiered decisions solve this by approving low-risk buyers instantly, capping spend for medium-risk buyers, and sending edge cases for a closer look.
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Trade credit is not a one-off decision. A supplier might approve a buyer today, then have five open invoices sitting unpaid at the same time, each with its own due date and chance of dispute. There are some basic rules to follow before businesses part with their cash.
A credit limit caps how much a buyer can owe at once, while payment terms set when the cash should arrive. For example, a new retailer might start with a £5,000 limit on 30-day terms, then move to £15,000 after a few clean payment cycles. This lets suppliers support growth without betting too much on an unproven customer.
A single £50,000 invoice can hurt if it goes unpaid. Ten £5,000 invoices can do the same damage, just more slowly. That is why suppliers use alerts and step-by-step approvals to control how quickly exposure grows when order sizes or order volume increase.
Improving decision-making does not require building a complex system. Most businesses improve outcomes by clarifying risk appetite and applying a consistent workflow.
Focus on practical fit: data coverage for your customer base, decision logic that matches your policy, monitoring triggers, and governance that makes outcomes auditable.
Even with solid credit checks, offering payment terms still creates a cash flow gap for the seller. iwocaPay closes that gap by assessing the buyer itself, paying the supplier upfront, and letting the buyer pay over time. In simple terms, the business gets paid immediately, avoids running credit checks in-house, and lowers the risk of late payments without making terms harder for customers.
