Credit Term Decisions in Freight Forwarding: Data-Driven Strategies for Improved Profitability

In the freight forwarding industry, customers—typically small freight forwarders, logistics brokers, or direct shippers—engage in transactions that are either prepaid (where freight and associated charges are paid upfront) or collect (where payment is made after delivery at the destination). To facilitate ongoing trade and manage the time gap between service delivery and payment receipt, freight forwarders often extend credit limits to their customers.

Traditionally, these credit decisions—both in terms of the amount and the credit duration—are determined by sales or account managers based on personal relationships, subjective risk perception, or historic behaviors. This manual and often static approach poses risks, especially in a high-value, thin-margin industry like freight forwarding, where delayed payments or defaults can result in significant financial losses.

By contrast, industries such as business banking, trade finance, and e-commerce have long adopted data-driven credit limit decisioning, using real-time customer behavior, payment history, and predictive models to inform limits dynamically. In these sectors, AI-driven credit engines and credit scoring algorithms have led to better credit risk mitigation, reduced defaults, and improved cash flow management.

However, in freight forwarding and logistics, the adoption of such data-driven frameworks remains nascent, and most freight forwarders still rely on static credit policies, exposing them to increasing risks, especially in volatile trade conditions or with unpredictable customer behavior.

Business Impact of Credit Terms

Credit Terms in Freight Forwarding: Financial Impact and the Role of AI/ML

Freight forwarders often extend credit terms to customers—such as shippers or consignees—to support ongoing business relationships and enable consistent trade flow. These credit arrangements typically consist of two key components:

• Credit Limit – The maximum value of unpaid shipments allowed at any given time.

• Credit Period – The agreed timeframe for payment, usually net 30, 45, or 60 days from the invoice date.

When credit terms are too generous, especially for customers with poor payment histories or weak financials—it can lead to an increase in Days Sales Outstanding (DSO). This directly affects the company’s working capital requirements, straining cash flow and elevating the risk of bad debt or non-payment.

In a low-margin industry like logistics, even a small rise in overdue or unpaid invoices can erode profitability significantly. Moreover, collections teams often spend hours chasing payments through emails and phone calls, adding to operational overhead and reducing efficiency.

How Can AI and ML Help?

AI and machine learning (ML) provide powerful tools to enhance credit term decisions in freight forwarding. By leveraging data and advanced algorithms, companies can shift from a one-size-fits-all approach to dynamic, data-driven credit management. This not only streamlines the decision process but also improves business outcomes. Key applications include:

• Customized Credit Assessments: Use ML models to estimate creditworthiness and assign tailored credit limits and terms at the individual customer level, ensuring decisions are based on specific customer behavior rather than a generic template.

• Adaptive Credit Limit Decisions: Regularly evaluate customer behavior—whether quarterly or semi-annually—and adjust credit terms accordingly to reflect the customer's latest performance and risk profile.

• Early Warning Systems: Implement ML-based trigger systems to alert teams to potential issues, initiating proactive collection efforts to recover dues before they escalate.

By integrating AI-driven decision-making into credit policies, freight forwarders can optimize working capital, reduce bad debt, lower operational costs, and enhance customer experience through smarter, more tailored credit offerings.

Challenges in Developing Credit Decision Models in Freight Forwarding

Building effective credit decision models in the freight forwarding industry presents several challenges, particularly due to the nature of B2B customer relationships and data limitations.

1. Limited and Biased Onboarding Data When new customers are onboarded, the primary focus of sales teams is often on closing the deal and initiating business, rather than capturing detailed, structured financial or behavioral data. As a result: Critical information such as financial health, ownership structure, or trade references may be incomplete or outdated. Input provided by customers or sales reps may be positively biased or unverifiable, especially if sourced manually or through informal channels.

2. Sparse Transaction History and Irregular Patterns Many B2B customers—especially small to mid-sized shippers or small freight forwarders—do not transact regularly. Their shipment volumes may be infrequent or seasonal, resulting in: Patchy booking and payment histories, which make it difficult to build statistically robust models. Difficulty in detecting consistent behavioral patterns needed for predictive credit scoring.

3. Data Infrastructure and Integration Gaps Fragmented systems (e.g., CRM, TMS, finance systems) often don’t communicate efficiently, making it hard to aggregate the data needed for modeling. Lack of integration with third-party data sources (e.g., D&B, Experian, trade payment databases) further limits the scope of external risk insights.

Summary

Credit term decisions in the freight forwarding industry have a substantial impact on both profitability and working capital. Despite this, the adoption of data-driven approaches, including the use of machine learning (ML) and artificial intelligence (AI)—remains limited across the sector.

There is a notable lack of publicly available research linking credit terms to non-payments or quantifying the financial impact of late payments on freight forwarders. Even general studies or benchmarks on default rates within the industry are scarce.

However, anecdotal evidence from industry practitioners suggests that late payments are common and contribute significantly to operational inefficiencies. Collections teams often spend considerable time chasing overdue accounts, increasing overhead. Moreover, when even a small percentage of customers default, the resulting losses can wipe out a disproportionately large share of profits in this low-margin business.

Implementing data-driven credit decision models can help freight forwarders better assess risk, tailor credit terms, and proactively manage the financial impact of late or non-payments—ultimately improving both cash flow and profitability.

References

1.      Atradius survey: significant uptake of trade credit insurance across UAE

2.      Benefits of Using Data Driven Credit Decisions | FreightAmigo