High-resolution data is rapidly changing the cargo insurance paradigm—and it’s about time.
While brokers thoughtfully deliver individualized care to each client, the limitations of traditional insurance mean they’re still selling them the same set of insurance products.
With high-res data, however, they are empowered to dynamically create tailor-made products that address the unique needs of each client—all while minimizing pricing fluctuations and delivering stable costs to balance sheets.
In short, the way they do business is changing for the better.
Traditional underwriters are flying blind
Think traditional insurance leverages historical data to underwrite risks? Think again.
For hundreds of years, our industry has operated on the data it can acquire from a few sheets of paper and historical claims data. That’s about as low-resolution as it gets—an unsettling thought when you consider underwriters often quote out coverage with million-dollar price tags. Intuition and personal experience have long influenced pricing, as well.
This approach is ripe for error.
Past performance is not a good indicator of future performance, and human intuition is fallible — experience varies wildly from underwriter to underwriter, after all. Let’s also not forget that underwriters are human; they can and will make mistakes. Perhaps the data they’ve gathered is incorrect or incomplete. Maybe the events of the previous year, such as catastrophic storms or pandemic-driven supply chain disruptions, don’t reflect today’s environment.
With so many unknowns, it’s understandable that underwriters must price insurance conservatively—they have to ensure the sustainability of their business.
In the end, this approach has contributed to the wild peaks and troughs we often see in conventional insurance pricing—extreme swings that leave clients frustrated and price some out of coverage altogether.
See Also: Shipping Industry Safety Keeps Improving
High-resolution data paints a more accurate picture of freight industry risks
While the insurance industry has been slow to digitalize, the freight industry has long embraced big data approaches to optimizing routes, reducing losses and improving driver safety. As a result, the industry is awash in data.
Leveraging embedded insurance to gain visibility into this data, an AI learning engine like Loadsure’s is generating more accurate, sustainable pricing with a more holistic view of the risks. Where traditional insurance priced policies against the quantities and types of shipments and losses in the previous year, it’s so much more useful when premiums are also informed by the shipment-level details of those loads, like routes, reefer temperatures and G-loads on cargo—all while simultaneously synthesizing the claims history to understand the correlations where losses occurred.
This is only the beginning.
Soon, location, weather and national crime database integrations will feed data into our AI learning engine, as well. This data may be meaningless when pricing traditional, annualized policies, but with transactional coverage it becomes both valuable and actionable. Automation and AI allow dynamic pricing models to be refreshed with new shipment and environmental data as often as need be. We can then bring together historical and real-time data and adjust on the fly. By catering to specific instances, brokers are empowered to deliver every customer a bespoke product.
Where traditional insurance could only see a shipment of glass, for example, high-res data can enable one tailored rate for a summer shipment of Gorilla Glass when roads are clear—and another for crystal chandeliers traveling a pockmarked highway during a winter storm.
Pricing granular risk? That’s just the tip of the iceberg.
What’s particularly exciting is that high-res data will also enable active risk management. Delivering actionable reports and real-time alerts, freight SMBs that lack the benefit of in-house risk management teams will get the crucial insights they need to suppress losses. If a police report indicates a high incidence of cargo theft at a truck stop, for example, an automated alert can instruct the driver to bypass that location in favor of a stop that’s less likely to pose a threat.
In time, this actionable information will become even more important than the paper itself—and clients will increasingly come to see brokers as active partners in their risk mitigation.
High-res data is the key to opening up new levels of service to brokers’ customers and delivering products as bespoke as the relationships they have already developed.
