Artificial intelligence (AI) is poised to become the modern underwriter’s ‘gadget’ toolkit in trade credit insurance. Imagine a future where underwriters receive AI-generated risk profiles that integrate real-time financial metrics, geopolitical events, and even market trends. The concept is exciting and feels within reach. Though still exploratory, these advancements hint at how AI might redefine the craft of risk evaluation and management.

“AI has strong potential to transform the export credit and investment insurance industry. By making risk assessments smarter and underwriting processes smoother, AI can help us handle uncertainties more effectively. This could lead to cost savings, improved profitability, and better strategic decisions, ultimately boosting global trade and investment to new heights,” said Arturs Karlsons, Associate Director at Berne Union.

A new era of underwriting: AI takes the spotlight

The strength of AI lies in its ability to sift through vast amounts of data and uncover patterns that no human could. With the right development and training, such a system would be able to review a decade’s worth of financial records for a client, cross-check them with live economic indicators, and deliver an assessment within moments. 

However, if AI is to gain widespread acceptance, particularly in this field, transparency will be crucial. Trade credit insurance operates under rigorous scrutiny, and stakeholders need to trust the systems in play; the opaque “black box” algorithms of the past don’t provide the needed level of visibility into the processes that were used to satisfy these requirements. 

Explainable AI (XAI)—a type of AI built to allow users to see exactly why a certain decision is made—is being developed to meet this challenge. For example, an XAI model that flags a client as high-risk would also detail the specific factors involved in that risk rating—perhaps declining revenues or exposure to volatile industries. This clarity could bolster confidence among regulators, clients, and insurers themselves.

What’s particularly intriguing is how these innovations could reshape day-to-day operations. If AI reaches its potential, insurers might see faster underwriting decisions, reduced costs, and a more responsive approach to client engagement. The workflows we’re used to today could feel outdated in the not-too-distant future.

From reactive to proactive: AI in risk management

Traditionally, credit insurers have had to play defence. Risks would emerge, and insurers would respond. AI hints at a future where that script could flip. 

Our innovation lab is exploring using tools like Natural Language Processing (NLP) to allow insurers to analyse unstructured data from news feeds, social media, or corporate reports to spot trouble before it happens. These tools could quickly detect certain trends that human analysts may not have the capacity to pick up—like a spike in negative sentiment around a particular industry in a given region—and proactively adjust coverage strategies. That’s the promise researchers are chasing.

The potential doesn’t stop there. One of AI’s most fascinating prospects is its ability to simulate how risks cascade through interconnected supply chains. Image having insights into the ripple effects created throughout the economy by a supplier defaulting on a payment. An AI model could help map this, highlighting which manufacturers and logistics providers might feel the impact next. These insights could provide a clearer understanding of risk propagation so that insurers would then be able to use them to help limit damage before it spreads.

Though much of this work remains experimental, the vision is compelling. It’s about shifting from a reactive to a proactive stance, and the possibilities that come with that shift are worth exploring.

The augmented underwriter: Man and machine in harmony

Let’s be clear: AI isn’t here to replace underwriters. It’s here to work alongside them, elevating what they do best. Ideally, these tools will handle some of the heavy lifting of data analysis, allowing underwriters to focus on the strategic, human elements of their role. Maybe the system will flag anomalies in a client’s financials but leaves the interpretation—the “what’s next?”—to the underwriter’s expertise. 

This kind of partnership could bring out the best in both humans and machines. AI would offer speed and precision, while underwriters provide context, judgment, and a personal touch. Together, they could create a more efficient and insightful process. The underwriter’s role might shift from data cruncher to strategic advisor and open up new opportunities to strengthen client relationships.

This evolution would come with challenges. Professionals would need training to make the most of AI tools. Yet, the potential rewards—greater accuracy, deeper insights, and enhanced efficiency—make it a challenge worth taking on.

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For all its promise, AI is not without obstacles. Cultural resistance is a persistent issue, and the idea of integrating AI into long-standing practices can be daunting. For organisations to truly benefit, there needs to be a shift in mindset—one that recognises AI as a partner rather than a disruptor.

Looking further ahead, technologies like quantum computing hold even greater possibilities for the field. These advancements could unlock capabilities that are currently out of reach, though for now, they remain speculative. The real focus should be on applying today’s tools to address immediate challenges and lay the groundwork for what’s next.

Trade credit insurers who prepare for these possibilities will be more able to adapt and thrive. The augmented underwriter—a collaboration between human intuition and machine intelligence—is an ambitious and exciting path forward.

To learn more about applications of AI in trade credit insurance, attend “The Augmented Underwriter: Can Artificial Intelligence Empower Further Credit Insurers?” panel at the online Tinubu Conference Day on 5 February. Get your ticket on this link.