For leveraged power generation assets, this can become a real financial constraint.
A waiting period after a plant breakdown needs to be covered by retained cash. So where a deductible layer remains uninsured, that risk sits on the owner’s balance sheet. The challenge is not only the size of the exposure but also the difficulty of transferring it when insurers lack sufficient granular visibility into the plant’s actual risk profile.
APP helped address this challenge through a live deployment of a data-enabled Machinery Breakdown Business Interruption (MBBI) Deductible Buy-Down, using operational risk data and equipment-level analytics to support a more responsive insurance structure. The result was a reduction in retained risk and a closer fit between the asset’s operational reality and its insurance programme.
THE CHALLENGE
In this case, the plant’s existing business interruption policy carried a general deductible of 60 days.
The machinery breakdown section carried a higher deductible of 90 days, leaving a 30-day gap of uninsured risk sitting on the policyholder’s balance sheet.
For the asset owner, and their lenders, this created an obvious problem. A major machinery breakdown event could trigger a long interruption, but part of that exposure remained retained rather than transferred. Without detailed plant-level risk data, the insurer would have had limited ability to price a product that reduced that deductible layer confidently.
This is where the value of operational risk visibility became commercially important. To move beyond a standard policy structure, the insurer needed a clearer view of the plant’s actual risk profile, not only at policy inception, but through a more granular understanding of equipment condition and risk behaviour.
THE APP APPROACH
APP provided the risk data and analytics needed to support the deductible buy-down.
Using the APP risk platform’s granular risk data and equipment-level analytics, the insurer was able to price and offer a more differentiated structure on the machinery breakdown section of the policy.
The platform gave the insurer a more detailed basis for understanding the exposure. With granular plant risk data and equipment-level analytics available, the insurer could price the buy-down with greater confidence.
This made it possible to reduce the machinery breakdown deductible from 90 days to 45 days. For the asset owner, this meant that part of the previously uninsured exposure could be transferred into the insurance programme, freeing up cash from their balance sheet.
RESULTS AND VALUE DELIVERED
The deductible buy-down created a measurable balance-sheet benefit for the asset owner.
By reducing the machinery breakdown deductible from 90 days to 45 days, the policy freed more than $10 million of previously uninsured risk from the policyholder’s balance sheet.
The policy encapsulated a Risk Management Incentive for the owner-operator team for the policy period. Based on the first three months of the policy period, the site’s risk score performance showed that the policyholder had already recovered tens of thousands of dollars through the policy’s live risk-score-link mechanism, equivalent to 76% of the maximum achievable incentive for that period.
This reflected measurable improvement in risk management behaviour on site: event management detail increased from 6% to 67%, half of the risk recommendations were closed early rather than held until renewal, and technical mitigations such as inspections helped reduce the risk of major failure by 14% in month four.
The case demonstrates the practical value of connecting plant-level operational insight with insurance structuring. Better risk data did not simply improve reporting. It enabled a different insurance conversation and gave the insurer greater confidence to price a product that reduced the asset owner’s retained exposure.
For the owner, the benefit was framed as a concrete reduction in balance-sheet exposure aligned with the asset’s actual risk profile.
A STRONGER insurance POSITION FOR asset owners
The significance of this case goes beyond one deductible structure. Power generation asset owners often carry complex risk exposures that are not fully reflected in standard insurance arrangements. Without granular data, it is harder for an insurer to reflect the plant’s specific risk position in the insurance structure. Better operational risk insight changes that.
APP’s risk platform provides insurers with the information they need to assess the plant more precisely. For asset owners, this creates a stronger basis for negotiating structures that better reflect how the plant is actually managed and where risk is being actively controlled.
That matters in high-value energy assets, where insurance, lender requirements, and balance-sheet exposure are closely connected. A clearer risk profile can support improved risk transfer, stronger insurance discussions, and greater financial flexibility.
LOOKING AHEAD
The deductible buy-down case reflects a change in how power generation risk can be managed and insured. Asset owners are no longer limited to presenting static information at renewal and accepting standard deductible structures. Where operational risk data is available, structured, and credible, it can help create insurance solutions that better match the plant’s real risk position.
For APP, the case demonstrates how engineering insight, equipment-level analytics, and insurance structuring can work together to deliver tangible value to asset owners.
Better risk visibility helped reduce retained balance sheet exposure. More broadly, it points to a future where asset owners can use operational discipline and data-backed risk management to improve how their assets are insured.
