What is credit management?
Credit management is the discipline of reviewing, analyzing and setting the terms of requests for credit for a business.
In the world of business-to-business (B2B) commerce, sales of goods and services are commonly made on credit with payment to come sometime after delivery. After a salesperson has made a sale for their company, it is the job of a credit manager to determine whether or not the company will allow the sale to be made on credit as well as how big the line of credit should be. This is called “credit decisioning.”
The goal of credit managers is to facilitate sales while minimizing risks for their businesses.
How does the credit process work?
The credit process begins with customer onboarding. In order to be considered for a line of credit with a business, a potential buyer must fill out a credit application. The application will list information about the potential buyer and will include a bank reference (ie. a contact at the potential buyer’s financial institution).
The credit management team will endeavour to gather valuable information about the potential buyer’s business. They may contact the bank reference provided by the potential buyer and inquire about their finances. They will also gather credit reports from the major credit bureaus to learn about their credit history. If possible, the credit management team will reach out to other businesses that the potential buyer has bought on credit from (referred to as trade references) in order to learn about their experiences with the business.
This process is very labor intensive, manual and slow. The credit management team faces two conflicting pressures: On one side, they are pressured to make a quick decision, so that their company can complete the sale and move towards collecting payment. On the other, they must gather enough information to make a responsible decision and prevent their company from providing goods and services that will not be paid for.
How do credit managers make decisions?
Credit Managers analyze data from a variety of sources to make educated credit decisions. After gathering information from a potential buyer’s bank, the credit bureaus and trade references, credit management teams will perform an analysis. This analysis may include calculating the business viability of the potential buyer, an indicator of the probability that the buyer will still be in business when payment comes due. The analysis may also include an accounting of their own firm’s outlay of credit. Credit managers endeavour to reduce their own company’s overall risk of cash flow shortages.
How does collections relate to credit?
Credit and collections activities are often combined into one department. Credit managers endeavour to set credit terms in such a way that buyers will be willing and able to pay on time, but when they don’t, collections activities begin.
Credit managers set the terms for collections activities when they initially grant the credit to buyers, and they direct the various enforcement actions that collectors will take to realize revenue for their company.
Key indicators of the effectiveness of the credit and collections department are Days Sales Outstanding (DSO) and percent overdue.
One crucial function of the collection department is getting older invoices paid off quickly, so that the buyer’s line of credit will be replenished and they can make further orders.
How to improve credit management
Credit Decisioning can be sped up by accounts receivable automation technology while producing better decisions. This is achieved in two ways:
Automating the onboarding process: The manual collection of credit application information, bank references, credit bureau reports and trade references is a slow process that involves multiple players working in coordination. Automation technology speeds the collection of material and can reduce onboarding time from weeks to days.
Artificial Intelligence powered credit analysis: AI can take in account a wide range of material including business viability scores, market intelligence and global economic trends to predict future payment likelihoods more accurately and more quickly than credit managers working without AI assistance.