‘Trade Credit’ is an arrangement between a buyer and a seller, allowing buyers to buy goods from their suppliers without having to make immediate cash payments. These purchases are made ‘on-account’ with deferred payment terms; most commonly used payment terms being Net 30 (payment within 30 days from the date of invoice), Net 45 (payment within 45 days from the date of invoice) and Net 60 (payment within 60 days from the date of invoice). Payment terms vary from industry to industry; for instance, it is not uncommon to come across payment terms of Net 180 in a high-value, slow-moving goods market, such as the Jewellery industry.
Trade Credit is the lubricant that greases the gears of business-to-business transactions. Buyers use trade credit to improve their cash flows, while sellers use trade credit to increase their sales. According to some estimates, in the US, about 97% of B2B transactions are made on credit.
Just as bankers need to evaluate the credit risk associated with their loan customers, credit managers of a business, also, have to evaluate the credit risk associated with extending credit to their customers. The process and methodology that a trade credit manager employs to assess risk are similar to those that bankers use.
In a business, it is the duty of the Credit Manager to play the role of a watchdog by being the Credit Controller: typically, Sales teams are incentivized to increase sales at the cost of overlooking credit risk; Credit Managers need to strike the right balance between sales and credit risk. An inappropriately high credit limit can put accounts receivable at risk, while an inappropriately low credit limit could result in loss of opportunity to sell. Credit Managers use credit limit (and sometimes payment terms) as the lever to control credit risk and strike an optimal balance.
Credit Managers use sophisticated risk models to quantify creditworthiness of customers; and depending on the creditworthiness, set credit limits and impose controls to limit credit exposure. These risk models are customized to the industry and business of the Credit manager. Various parameters are used in these risk models to ascertain credit worthiness, even though the parameters used, vary across industries, they generally fall into the following buckets:
Income Statement, Balance Sheet and Cash Flow Statements are used to analyze financial health. Key financial ratios (some of them being industry-specific) are used in the model as indicators of financial health.
For existing customers, their payment history is used as a proxy for predicting future payment behavior. KPIs such as Average Days Late (ADL) are used to quantify payment behavior.
Some credit managers, use in their models, some indicators about a business’ operations such as age of business, length of relationship as a customer, number of employees, number of customers et al.
Sometimes, it is important to consider environmental factors such as the country of operation of the customer (factor in political and regulatory risk), region of operation (if it is prone to natural calamities) and other such factors that have a bearing on the customer’s ability to pay back.
In addition, it is not uncommon for credit managers to rely on credit bureaus such as D&B, Experian, Equifax et al for an independent evaluation of the customer’s risk profile.
A good risk model uses an optimal number of data points to quantify the credit risk of a customer and the quantitative score computed by the model is used for decision making. Following activities are part of a typical credit decision-making process:
Figure 1: Credit Decision Making Process
Most of these activities (except for the first and the last two) are automatable to a large extent and a good credit management software solution can help Credit Managers improve the efficiency of their decision-making process through automation.
At HighRadius Corporation, we have developed a suite of software tools called the ‘Credit Decision Accelerator’ to automate a large part of the decision making process, freeing up valuable time for the Credit Managers and Analysts, so they can focus on the risk model.
In a sequel to this post, I will discuss how automation can help improve the efficiency of the credit decision-making process.
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