Many suppliers have dedicated credit teams for measuring the credit risk of their customer portfolios. But how will they assess the credit risk? What’s the procedure of credit risk evaluation?
To answer these questions, organizations build a credit control policy that essentially acts as a framework for credit teams. Every organization has their specific credit policy, but overall, a credit policy provides clearly articulated guidelines about:
A structured credit policy ensures that the credit team uses a standardized method for managing a customer’s credit risk. This leads to consistent credit decisions and eliminating compliance issues because there is an audit trail. Additionally, a credit policy helps to onboard new talent onto the credit team because they can quickly consume the guidelines to start evaluating credit risk.
Credit risk management, often considered to be a back-office function, serves an essential purpose. Credit teams help to protect an organization financially as they are vigilant against risk. If they onboard risky customers it might lead to a higher bad debt reserve.
On the other hand, credit teams are expected to work closely with sales to boost revenue growth. In a nutshell, credit teams wear multiple hats by becoming the guardians of cash and co-pilots of revenue growth.
Let’s look at the significant challenges faced by credit teams while evaluating credit risk for their customers.
Credit risk monitoring techniques involve a lot of communication between ERPs, spreadsheets, and credit reports. As a result of the manual effort involved, credit teams lack end-to-end visibility on their portfolio risk. This means that if the credit risk fluctuates, they won’t be able to track that change.
Imagine the risk manual credit management imposes on the organization’s bottom line. It is recommended that credit teams evaluate their customers on a real-time basis with automated credit management systems.
Whenever an order gets blocked due to insufficient credit balance, the sales team might insist the credit team releases the order to ensure a good customer experience. In such cases, credit teams release the order based on a verbal payment commitment which isn’t always reliable.
Credit teams can proactively handle blocked orders by leveraging AI capabilities. AI helps credit teams predict upcoming blocked orders to proactively recover a partial payment from the customer before releasing the order
HighRadius Credit Software automates the credit management process, enabling credit managers to make highly-accurate credit decisions 2X faster and enable faster customer onboarding with 4 primary components: configurable online credit application, customizable credit scoring engines, credit agency data aggregation engine, and collaborative credit management workflow. Along with that, there are a lot of key features that should definitely be explored some of which are online credit application, credit information aggregation, automated credit scoring & risk assessment, credit management workflows, approval workflows, and automated bank & trade reference checks. The result is faster customer onboarding, better internal collaboration, higher customer satisfaction, more targeted periodic reviews, and lower credit risk across the company’s customer portfolio.