How continuous risk data is reshaping renewable energy insurance underwriting
This piece is by Alistair Moodie, Chief Product Officer, SAMP Risk, the Lloyd’s Lab Insurtech.
As a mechanical engineer with deep expertise in power generation, Alistair spent over a decade on site, where he actively drove critical decisions across operations, maintenance, and engineering to manage equipment failure risk. This firsthand experience solidified his commitment to leveraging data as the essential tool for making effective, risk-informed choices.
Today, Alistair leads the product development of SAMP Risk, a risk analytics service platform for insurers and brokers, focused on risk analytics for renewables and power generation. As a Lloyd’s Lab alumnus, Alistair and his team have developed risk analytics that estimate the probability of failure of site equipment, provide site teams with live risk mitigation insights, and enable innovative insurance products that improve risk transfer for independent power producers.
Renewable energy insurance is usually priced based on a site survey perhaps once every three years, but this varies by site. In the interim, it is usual for remote/desktop surveys to be conducted at renewal. For an industry that is growing and generally lightly-staffed, this rhythm is increasingly out of step with the risk it’s meant to price.
Why does renewable energy risk data go unused?
Data isn’t the problem, it’s how that data reaches the insurance market that has been the barrier to more connected insurance.
Renewable generation sites typically produce three separate streams of risk information: periodic engineering inspections, real-time SCADA (Supervisory Control and Data Acquisition) feeds, and day-to-day management activity.
These streams sit with different teams, update on different cadences (from per-second to quarterly), and describe risk in incompatible language. An engineer’s “high risk” and an underwriter’s “high risk” frequently describe two different things. Without a common reference point, attention goes to whatever issue is loudest that week, and premium defaults to whatever a static survey said months — sometimes a year — ago.
What is a Plant Health Index?
To address this problem, a data scoring solution is emerging, in much the same way driving behaviour data is used in motor insurance. Our Plant Health Index offers a single, continuously refreshed score out of 100, combining technical risk — probability of failure multiplied by consequence, calculated per component — with a behavioural modifier reflecting how actively a site manages that risk day to day.
The design principle is traceability. A shift from 78 to 74 isn’t just a number moving; the platform identifies exactly which component, finding or action drove the change. Because SAMP Risk’s connected insurance model prices against that live score rather than a point-in-time survey, the explanation behind a score change feeds directly into how the site is underwritten.
What happens when renewable energy sites can see their risk score?
Across live deployments, connecting visible risk data to insurance terms has driven measurable behavioural change:
· Investigation detail on unplanned events improved by 61 percentage points within five months of deployment
· Half of all risk recommendations were closed early, rather than deferred to renewal
· Modelled risk of a major outage fell 14% within four months
In one case, a risk invisible to SCADA and missed during routine maintenance review was flagged by the platform; left unaddressed, it would likely have caused a significant unplanned outage. In another, the system identified an elevated-wear gearbox that had rated normal. Manual inspection confirmed the fault, and the unit was replaced before failure — the kind of issue that, undetected, typically becomes an insurance claim.
How does real-time data change insurance underwriting?
The same score that is driving on-site behaviour is also the mechanism an insurance policy runs on — providing the live basis for premium and terms. As a site’s score improves, pricing can move with it, rather than waiting for the next renewal to catch up.
This has measurable balance-sheet impact beyond the insurance line itself. Shortening the reserve periods lenders typically require against business interruption risk has freed millions in working capital across individual sites — funded by demonstrated risk reduction.
Managing and pricing risk continuously
Fundamentally, the raw data for better reliability and better-priced risk already exists on-site, proven in deployments across live sites in the United States and South Africa. What changes the economics is connecting it — turning both reliability and insurance from once-a-year exercises into something managed, and priced, continuously.
Be the first to comment