Risk management has evolved more rapidly in the last few decades than anyone could have predicted. It’s not groundbreaking to say that emerging technologies have transformed the way assureds and insurance brokers manage risk. Yet, the progress we’ve seen is unprecedented, and AI-powered solutions now dominate the market, with 91% of respondents to a 2024 survey reporting that they’re either already investing in, or considering, AI. 

Alongside the increasing sophistication of automation tech, AI has revolutionised how we use data in the insurance industry, granting it new power and potential — so what impact can we expect these factors to have on risk management?

Conventional risk management can’t keep up with an evolving market. 

As the global supply chain has grown more complex and multi-faceted, so too have the risks it faces. Cargo fraud is prolific, with reports of double brokering up 14% in the third quarter of last year, while a number of ongoing geopolitical factors induce sanctions, strikes and shortages that wreak havoc on supply chain businesses. Meanwhile, 2024 saw 27 extreme weather events in the US alone, amounting to losses exceeding $1 billion per disaster. 

These are risks-in-motion, with parameters that constantly shift, and conventional risk management is simply no longer fit for purpose. This traditional approach focuses primarily on insulating businesses from risk through insurance, with minimal provision for preempting losses before they can take place, or expediting loss recovery. 

Not only do conventional risk management methods fail to foresee or minimise risk, but the data and technology they rely on limit the usefulness and accuracy of any insurance policies issued. With only a static dataset to work from, traditional policies are impossible to price at a point that actually reflects the specific risks in question.

As well as being inaccurate, manual pricing processes are time consuming and labour intensive. This makes it impractical for insurance brokers to offer supply chain businesses heavily customised policies, and hikes up the price so they’re too costly to purchase — despite the clear need for them in the transportation and logistics community.

The application of data and AI: what does it look like? 

The need to address pain points like inaccurate pricing and inaccessible policies has given rise to a huge number of AI-powered solutions, including Loadsure, the InsurTech MGA I co-founded seven years ago. In fact, the value of the “AI in insurance” market is projected to reach $80 billion by 2032, so there are many, diverse applications of data and AI in risk management to observe today.

Here are some of the most transformative ways AI and data are being used by pioneers in risk management: 

• Analysing large datasets. With AI tools, a huge volume of data can be processed in minimal time, allowing for risks to be assessed more effectively. This enables the adoption of dynamic pricing models, which equip insurance brokers to offer hyper-accurate and sustainably-priced policies. 
• Driving data-powered innovation. The trends extrapolated by AI can inspire the development of new risk management products and services that are driven by data, and tailored to the specific challenges experienced by different sectors of the market — like usage-based or personalised policies. 
• Creating potential for active risk management.Real-time data sets also make loss prediction and prevention a reality for supply chain insurance businesses, through access to actionable insights that can empower assureds to reduce risk and even bring down insurance rates.
• Improved overall efficiency. In the right hands, AI can optimise processes, reduce time and labour costs, and even entirely automate some tasks — like handling low-value claims — which minimises the touch points for insurance providers, and expedites outcomes for claimants. 
• Enhanced customer experience. In increasing efficiency across the board, enabling the development of dynamic pricing and freeing up valuable resources for insurance brokers, AI inevitably improves the customer’s experience of risk management solutions.

What about the role of automation? 

In tandem with AI and machine learning, automation is enabling the definition of risk management to extend beyond insurance, facilitating a more holistic approach that incorporates loss recovery too. For instance, many insurance providers and third-party claims handlers are completely remodelling their claims processes to incorporate automation technology. 

The impact of automation in this context is significant and varied — not only for those handling the claims process but primarily for assureds. Some of the key advantages the industry can benefit from include: 

• Faster handling times. Automation can handle certain segments of the claims workflow, reducing time and labour costs while streamlining the settlement process, so insurance providers are free to focus on client relationships and more complex cases. 
• Quicker payouts. With expedited settlements, supply chain businesses are made whole again more quickly, so the loss they’ve experienced is less disruptive and they won’t need to plug the financial gap with their own money while waiting for a payout. 
• Transparent processes. Communications can be automated throughout the lifecycle of the claim, keeping policyholders in the loop on claim status, and reducing touchpoints as they’re less likely to need further updates from their insurance provider.
• Consistent outcomes. With the capability to process and interpret vast amounts of data, automation improves the accuracy and consistency of outcomes, eliminating human error and ensuring the fair and uniform treatment of all claims.
• Valuable claims data. For brokers, automated claims processes also deliver access to valuable claims data, which they can use to advise their clients, sharing actionable insights that will reduce future risks.

As well as this transformational effect on claims, automation technology offers further benefits through feats like personalised communications, optimised data collection, low-touch reporting facilities and generally increased efficiency — with all of these use cases resulting in considerable cost savings. 

As this technology evolves, so too must the insurance industry.  

Recent research from Gallagher Re found that over a third of InsurTech deals went to AI-centered InsurTechs in 2024, so the time to adopt this technology is now. Those who fail to evolve will find that their bottom lines suffer, as more efficient and affordable risk management solutions — powered by AI, data and automation — set a new standard for the freight community. 

With entirely new categories of risk management entering the market, like Holistic Freight Protection, it’s clear the future lies in a comprehensive approach which places as much weight on loss prevention and recovery as it does on the provision of accurately-priced insurance products.