B2B credit management has evolved since 2019. Here’s how to ensure your credit department succeeds. 

The COVID-19 pandemic drastically changed the world, businesses, and their credit departments. It reshaped our economy. In order to meet the changing business landscape, credit managers have adapted quickly to maintain their companies’ financial stability. 

Let’s briefly review the economy before the pandemic started. This will give us a clearer picture of the changes that have happened and the difficulties B2B credit managers now face. We’ll look at how your sales team can become a credit ally and close with tips on how to decision today’s B2B credit with success.

Pre-Pandemic Stability

Before COVID-19, the economy experienced comparatively stable growth. Companies were generally optimistic about their clients’ creditworthiness. The approval process for B2B credit managers was a relatively simple routine. They usually assessed customer creditworthiness based on financial statements, credit reporting, and industry benchmarks. Once a credit limit was approved, customers were generally given net payment terms.

Pandemic-Induced Shifts

The pandemic triggered a series of economic shifts that profoundly affected B2B credit practices. Government stimulus programs, supply chain disruptions, and inflation surges all contributed to a climate of uncertainty and volatility. 

According to the National Association of Credit Management (NACM), total bankruptcy filings increased 18 percent year-over-year (YoY) in 2023. 

As a result of these changes, businesses became more cautious about extending credit and credit managers had to adopt a more rigorous approach to risk assessment. 

6 Key Changes in B2B Credit Management

In-Depth Credit Risk Assessments

Economic changes caused credit managers to become more reliant on data analysis to assess creditworthiness. This includes using financial modeling tools to assess a company’s ability to meet its debt obligations. Credit bureaus and alternative data sources are also leveraged to achieve a comprehensive view a customer’s financial health.

Tighter Credit Terms

As businesses become more risk-averse, they are tightening their credit terms. This can involve shortening payment terms (e.g., from net 60 to net 30), reducing credit limits for existing customers, and issuing lower initial credit lines for new customers. According to a March 2024 report by HighRadius, 52 percent of companies seek extended terms – quite the opposite view. The same report shows that 17 percent of customers blatantly ignore credit terms while another 48 percent intentionally delay payment. This can make building strong customer relationships difficult.

Increased Use of Credit insurance

The rise in economic uncertainty has led to a surge in demand for credit insurance. Credit insurance protects businesses from monetary loss if a customer defaults on their payments. A 2023 survey by AU Group shows that since the third quarter of 2022, the number of business failures in almost every region of the world has risen. In line with that statistic, credit insurers expect growth in their sales over the next six years.

Growing Use of Digital Credit Tools

The pandemic has accelerated the adoption of digital credit tools and automation. Tasks like processing credit applications, credit checks, and collections are now being completed faster and allowing credit teams to focus on exception management.

Collection Challenges

The pandemic caused many businesses to experience cash flow disruptions. It’s made it more difficult for some companies to meet and/or maintain on time payments.

Cash Flow Management

Businesses are focusing on more effective ways to manage their working capital. This can include reworking their collection processes and closely tracking inventory levels.

Opportunity Emerges

All these changes have significantly affected credit managers and their teams. Now, they carry heavier workloads and face increased pressure to mitigate credit related risks. They also need to be able to adapt to rapid changes that may happen in today’s economy. 

While these changes may have increased the burden on credit managers, they’ve also created opportunities for collaboration with sales teams. By working together, credit managers and sales teams can better service their businesses and customers.

5 Ways B2B Credit Managers Can Seek Help from Sales

In today’s risky and fraud-ridden environment, the sales team support in customer onboarding and credit is vital. Credit and sales teams must collaborate to ensure a positive and seamless customer experience. Here are some tips to foster better collaboration:

Educate for an Improved Understanding

Sales teams are crucial in helping gather customer information to assess creditworthiness. Credit managers can help sales teams understand the importance of collecting this information. Sharing its use and how having it can make the approval process faster helps, too. 

Develop a Standardized Form  

A standardized customer information form ensures sales teams collect all the required information. This can help streamline the credit approval process.

Encourage Proactive Customer Updates  

Credit teams must stay updated on customer developments. Encourage the sales team to proactively share any relevant customer updates with the credit department. Discuss what information is “relevant”, so everyone is on the same page.

Have a Joint Review Process  

Joint sales and credit reviews can ensure both teams understand customer creditworthiness. They can help prevent incidents where a customer is given an okay by sales and later is deemed to be a credit risk. At the same time, joint reviews will strengthen the relationship between sales and credit while improving the customer experience.

Foster Open Communication and Trust 

Open communication and trust are essential for effective collaboration between teams. Credit managers should be available to answer sales teams’ questions and provide guidance on any credit-related matters.

Is This the New Normal for B2B Credit Management?

It appears this “new normal” of post-pandemic business is here to stay, and it’s changed credit management for the foreseeable future. Because of this, we must have a more strategic and data-driven approach to B2B credit management. Those credit teams that adapt to these changes and improve collaboration with sales will be well-positioned to thrive in today’s economy. Furthermore, those who stay flexible and committed to delivering exceptional service will aid their company’s success. Will your credit team be the ones to hold revenue back or help drive it forward?

About the Author 

Tracy Mitchell currently holds the position of Director of Accounts Receivable at Trinity Logistics. She has worked at Trinity for nine years, with over five years of those in credit management. She holds a Credit Business Association (CBA) designation. With a deep understanding of the industry’s dynamics, she has firsthand knowledge and provides the company with invaluable insights into the complexities of credit risk assessment, collections, and sales alignment. 

The following is an opinion article on AI in supply chains, written by Russ Felker, Chief of Technology (CTO) of Trinity Logistics.

Artificial intelligence (AI) continues to grow its presence in our everyday lives, businesses, and now, supply chains. In a recent MHI Annual Industry Report, 17 percent of respondents said they use AI, with another 45 percent stating they will begin using it in the next five years. And of more than 1,000 supply chain professionals surveyed, 25 percent stated they plan to invest in AI within the next three years. While AI in supply chains has its benefits, it continues to be overhyped as a replacement for human cognitive abilities.

AI in Supply Chains: We Need to Change Our Focus

The technologies leveraged by today’s AI offerings fall flat when applied to the complex day-to-day of supply chain interactions. We need to stop chasing the inflated promises of artificial intelligence and start focusing on the very powerful pattern recognition and pattern-application technologies marketed today as AI to support our teams more effectively. Instead of focusing on AI, we need to reorient on CAI (computer-aided intelligence).

Now, this might seem like a semantic argument, and to a certain extent it is, but the difference between artificial intelligence (AI) and computer-aided intelligence (CAI) is distinct. You might ask, “What does it matter if the technologies are being put in place and create efficiencies?”  “So what if it’s called AI?”  I would say it makes all the difference in the world.

What AI in Supply Chains Currently Does Well

First off, let’s talk about the technologies backing the products that include AI.  As with many technology implementations, they are, by and large, applying rulesets to data. Being able to quickly process a defined pattern against a large data set is both no mean feat and hugely beneficial in a supply-chain setting. In the end, however, these implementations are no different than a rules engine – albeit one with a high degree of complication. For example, take an area of the supply chain that has had this form of technology applied to it, quite successfully, for many years – route optimization.  

Optimizing a single route is relatively simple but optimizing the routes of multiple vehicles in conjunction with related schedules of item delivery commitments and layering in things like round-trip requirements and least amount of non-productive miles (miles driven without a load) and the level of complexity moves well beyond what an individual could do in a reasonable period of time.  What can take on this type of task is a processing engine designed to apply complex patterns within a given boundary set – and that’s what current implementations of AI can do. And they do it well.

Why AI Can’t Replace Humans

The first problem comes in when we examine the stated goal of AI – the ability for a machine to work intelligently. The difference between hype and reality is in how we interpret a keyword – intelligence. Even the most recent and hyped AI systems continue to fail at the same core intelligence functions such as understanding nuanced context and broader application of existing patterns.  

Take Gato from DeepMind, a division of Alphabet, as an example. While it can examine an image and draw basic conclusions, the context and understanding are both entirely missing from its analysis. Tesla provides another example where a driver had to intervene as autopilot couldn’t recognize a worker holding a stop sign as something it should avoid. These limitations minimize the tasks for which AI technologies can, and should, be leveraged.  

The second problem is related to the first. The acceptance of “AI” from teams has been wrought with, at a minimum, intense change management and, in the worst case, rebellion. If you are bringing in AI to a team, why wouldn’t they draw the conclusion that your goal is to replace them? To start down the path of both realistic expectations from senior management and more widespread adoption of technology, we must change the approach we take with stakeholders impacted by implementations of AI. We need to talk about CAI.

It’s Time to Set the Stage for CAI

Just the acronym alone talks to a much more practical and achievable marriage between a person and a computer. It’s not the computer that’s intelligent; it’s the person using the computer. What a computer can be taught to do, is to effectively deliver relevant information to a person at the time they need it based on their job function and recognized point in the process. So instead of using a technology such as a recommendation engine to pick a product you might like or a movie you’re likely to want to watch, let’s turn our focus to delivering salient business information to our people. We can effectively use analytics and machine learning to create data recommendations and deliver those recommendations directly to users in their primary applications at the right time in their process, so they don’t have to go find data in multiple reports or sites. Once a pattern is recognized, by people, and the data is organized correctly, again, by people, we can use things like machine learning and analytics to deliver that result set effectively and consistently.  

What this approach achieves is reduced interaction by a person and the machine reclaiming time for people to connect with customers outside of transactional conversations. By providing relevant data in-process, you make your team more efficient in their use of the system and create more opportunities for person-to-person interactions and relationships. The goal of any system implementation should be to reduce the time needed for a person to interact with it to achieve the desired result. This is different from having the perspective of the machine doing what a person does – which can be a misguided goal of AI. Instead, the system needs to be built to strategically leverage AI in areas that support the reduction of repetitive, rote work, enabling teams to focus on higher-value work.

A 3PL Focused on People

As a 3PL, a large part of our work tends to gravitate toward the identification and management of exceptions, but many times that is reactionary. We can leverage the technologies present today to enhance exception identification and management. Via AI-enabled supply-chain systems, information can be more present for teams to apply their intelligence, experience, and skill to solving issues optimally. The ability to recognize early in the life of a load the potential of a delayed delivery enables teams to make proactive adjustments with the receiving facility and the recipient. We can gather documents automatically and provide the information in a consumable fashion reducing the amount of manual effort to extract relevance from the documents.

As a 3PL we rely on two primary skills – intelligent use of data and building and maintaining relationships. Neither a computer nor an algorithm can do either of those alone, but a person backed by a Computer-Aided Intelligence system can. Creating systems that focus on CAI is what allows Trinity’s true source of intelligence, our team, to shine and deliver consistently phenomenal results for our customer partners. Now, you might be the exception and prefer to converse with a chatbot, but I’m guessing if you read this far, you’d rather talk to a person – which is what you get when you call Trinity – a person, backed by computer-aided intelligence systems, who is ready to do the work to create a relationship with you and deliver phenomenal results.