Credit management as a function focuses on onboarding new customers, periodically assessing the customers, and releasing possible order blocks. The majority of these operations involve a lot of manual intervention. As a result of this, credit teams dedicate a lot of their time to clerical tasks instead of the core credit decisions. Let’s take a look at the significant challenges encountered by credit teams:
B2B credit teams have to onboard new customers across the globe, and they usually do it with credit applications. Most of these credit applications are paper-based, and customers often miss out on adding important business information. As a result, credit teams have to interact with customers multiple times to capture correct and complete information. Additionally, slow bank and trade reference verifications lead to a delayed customer onboarding process, impacting the overall customer experience.
Credit teams have to log into D&B, Experian’s portals to manually download every single credit report. Additionally, they need to pull reports from regional credit bureaus to assess the risk. This is most common in regions such as LATAM or Europe. Pulling credit reports for every portfolio at a global level can be difficult. After downloading the reports, credit analysts manually review the credit ratings and financials and calculate the credit score. Credit approvals become slow and erroneous because of the multiple stakeholders involved, therefore increasing the risks involved.
With periodic reviews, credit teams struggle to identify at-risk customers. This was working out when the economy was stable, but periodic reviews are no longer a solution in the current turbulent economy. This is partly because credit teams face constant unpredictability while identifying portfolio risk, which might fluctuate at any time. With 1000s of customer portfolios, it’s difficult to regularly review and track the frequent changes in their credit profile.
When a customer exhausts their credit limit, their upcoming orders are blocked. In such scenarios, credit teams often release those blocked orders based on Sales’ insistence, without any payment commitment – this is highly unreliable. Otherwise, they wait for the collectors to collect a partial payment, leading to a shipment hold for the customer – resulting in a poor customer experience.
So, now the question arises, How do you achieve a full-proof credit management system to tackle all these problems?
The answer to this question is by leveraging automation. In order to accomplish these tasks and tackle the complexities in hand, modern B2B credit management teams are becoming a lot more data-savvy and using advanced tools like Artificial Intelligence (AI) and Robotic Process Automation (RPA). They are moving away from paper-based analysis and relying on automation-driven insights to reduce bad debt and improve cash flows.
An efficient credit management system should bring transparency and proactivity to credit risk management. It should be capable enough to streamline all business processes linked to credit management and help business organizations deal with their everyday challenges in the following ways:
Furthermore, to choose the right credit management system for your business, you need to identify the problems that your existing credit management system is not solving. It also varies as per your business requirements because every business has unique problems. The more specific the problem identification would be, the better decisions could be made while choosing the right credit management system.
There are four major parameters that can help you choose within on-premise and AI-based cloud solutions for credit management:
|Parameter for Evaluation||On-Premise Solutions||AI-Based Cloud Solutions|
|Risk & Security||
With HighRadius’ Cloud-Based Credit Management System powered by AI, credit teams can achieve 100% real-time credit risk monitoring to ensure lower bad debt by tracking changes in customer credit risk and payment behavior.
AI can be leveraged to predict upcoming blocked orders based on past order volumes and payment patterns. Credit teams either release or hold the orders based on AI-based order release recommendations.
Additionally, AI-based Credit Cloud software helps credit teams to onboard customers faster by leveraging a highly configurable Online Credit Application.
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.