When I founded Loadsure, one of my goals was for the business to establish itself as best-in-class at pricing. Today, dynamic pricing is a key part of our offering, as well as a major differentiator between Loadsure’s innovative solution and the cargo insurance offered by the more traditional players in the industry. But why, at a time where risks are more diverse and variable than ever, are so many insurers still relying on outdated pricing models?

In a recent survey of specialty and commercial insurers across the US and UK, less than 20% of respondents described their pricing processes as “data driven”. Given the direct line from pricing to profit and loss, this statistic is surprising. Insurance has always been slow to evolve, be it in adopting new technologies or optimising processes, but when it comes to pricing, the reluctance to stray from conventional market practice is bad for business.

So, what are the limitations of traditional pricing models? 

In simple terms, traditional pricing models rely on a non-standardised data set. This data is only up-to-date at the very moment it’s captured, resulting in a lag that inevitably distorts risk profiles. Additionally, this model relies on data collected from the assureds in proposal forms — and while insurance providers add their own questions, they can’t anticipate unknown risk factors, so the data delivered in these forms is finite.

While periodic repricing exercises do somewhat improve data accuracy, insurers are still only made aware of changes in the market once they’re visibly reflected in the data, so they’re always playing catch up. Predictably, using this model leads to mispriced policies, which isolate the portion of the market that can’t afford those products. Now there’s a smarter option available, these businesses usually turn to competitors using lead metrics to accurately calculate the risk, via dynamic pricing models like ours. 

On top of eroding profits, traditional pricing models are virtually unscalable. They require manual data collection, and they’re so time consuming that insurance providers often set a minimum scale to ensure the work is financially worthwhile. In the context of cargo insurance, that could mean being unable to serve SMBs in the freight community with coverage needs that don’t meet this threshold. 

Yet, upgrading your pricing model is far more complex than simply “implementing a new approach.” 

One of the key mistakes businesses make when upgrading their pricing model is forgetting the importance of data maturity. It’s impossible to arrive at accurate, data-driven pricing overnight;  instead, there’s a journey to the successful implementation of a new solution. 

When Loadsure went to market, we had analysed historical data using that traditional reactive approach, but we took our basic-level data and put it through various development initiatives to make it more purposeful. Eventually, we were able to move onto systematic-level data, with advanced integrations and real-time processing. Only after taking these steps could we consider bringing in machine learning models which can actually expand the capability of our data. Today, we have sophisticated analytics and alerts in place that facilitate our now hyper-accurate pricing model. 

For insurance providers, every step in this journey is crucial. You can’t implement an untested model and expect instant results — you’ve got to start where the data is today, and move through each stage of the data maturity lifecycle.

Loadsure takes data-driven pricing one step further, using AI and machine learning to deliver proactive risk management. 

With high-resolution data, we have access to the granular details of an insurance risk, whether it be direct characteristics or secondary factors that might influence the behaviour of a risk under certain conditions. This level of data maturity, alongside the adoption of AI and machine learning, is what enables us to smooth out the normal peaks and troughs in market cycles.

In short, our dynamic pricing model equips us to deliver consistently priced cargo insurance policies which assureds can reliably afford, eliminating the chances of huge unforeseen losses triggered by specific events, like natural catastrophes.  

Yet what really sets Loadsure apart is how we’re harnessing the potential of this technology to deliver proactive risk management. We’re prioritising collaboration around data, building partnerships with groundbreaking companies like WeatherOptics (a weather risk and climate intelligence platform) who can provide actionable insights on specific risk factors.

This is a key pillar in offering Holistic Freight Protection.

Insights like this not only allow us to underwrite with an unprecedented level of granularity, but also help our assureds to reduce the likelihood of a loss in the first place. For instance, they might implement specific cautionary storage measures in a high flood risk area, or alter shipping routes to avoid supply chain disruption. The reduction of loss events is hugely positive for both assureds and insurance providers: disruption to business decreases,  there are fewer claims filed and, as a result, lower insurance premiums.

Ultimately, technologies like AI and ML are optimising pricing beyond recognition. When successfully adopted, they allow insurance providers to move from a slow, reactive model to a proactive, accurate one, reducing labour-intensive pricing processes and opening up new streams of revenue amidst the assureds. 

Leveraging rich data, AI and automation is a foundational tenet of our offering, and it’s through this technology that we’ve been able to develop a whole catalogue of revolutionary cargo insurance products.