Here at the SENSE Consortium, we are passionate about the topic of sensors and real-time data and its many benefits for the insurance and risk management industry.
What is our mission?
Using internet of things sensors and real-time data, to halve the cost of losses through prevention, thereby keeping companies, factories and manufacturing open and adding to the global economy - all while enabling insurance companies to offer better prices and services to their customers and improve ESG corporate responsibility.
There is much talk in the insurance industry about how sensors and real-time data will disrupt traditional risk management, underwriting, pricing and claims management. This white paper focuses on pricing in the early years of launching a Commercial Property Insurance ‘Internet of Things’ (IoT) product, before reflective claims data is available.
The rationale behind this focus on price is to drive adoption of sensors within Commercial Property, to gain access to new data sources and overall to reduce the total cost of risk through ameliorated risk management action.
Noted that within a risk management function, there are many competing priorities of where to spend limited funds to respond to a risk survey’s recommendations.
The challenge …
We are seeing an emerging number of use cases in commercial property insurance and yet, the scaling and overall traction of this technology and data is still in its infancy. One obstacle to scaling is the ‘chicken and egg’ situation on pricing. To gain traction, there is an overarching sentiment from commercial property owners that insurance products need to offer price discounting to persuade a critical mass of customers to start using Internet of Things (IoT) devices. However, traditional pricing methods involve using significant past claims data, which does not exist until this critical mass of customers have used the product for several years.
This whitepaper presents a solution to the conundrum of how to offer price discounting to support the adoption of IoT devices via a formula that adjusts risk cost estimates using a core of product-level expert judged parameters. i.e. It does not require underwriters to view/opine on individual properties.
Shape of the Discount
The key aim of the formula is to reduce the discount as the technology ages and falls in line with future properties’ standard technology and/or gets used less and/or deteriorates.
Below are example parameters to demonstrate how the IoT Discount varies as technology ages for Actual Spend Density less, equal, and greater than the Target Spend Density.
Vagueness of Formula
Several of the variables/parameters in the formula have been left deliberately vague…
Technology Age: Could be time since purchase, time since being considered cutting edge, or a combination (e.g. average) of the two.
Commercial Property Floor Space in Square Feet: This could be for the entire portfolio or for each property individually, depending on available data and pricing engine sophistication.
Application of the ‘IoT Discount’: If Floor Space is entered separately for each property, then a separate discount can be calculated for each property. If we define Qualifying Technology by peril, then a separate discount can be calculated for each peril. If working at peril level, consider parameterising MaximumDiscount and TargetSpendDensity by peril.
The Human in the Loop
A key selling point of this approach is efficiency: infrequent parameterisation, no human case-underwriting requirement, the discount is automated within the pricing algorithm.
A likely ‘expert judgement’ parameterisation team would be an underwriter and a pricing professional. The underwriter estimates the shape of the risk reduction against all of the variables, and the pricing professional ensures that there is no failure of communication between the underwriter’s thoughts and what comes out of the algorithm. The underwriter may also be responsible for keeping the list of Qualifying Technology up-to-date.
Proof of Technology and Expenditure
To keep the insurer’s expenses low, the client will evidence their Qualifying Technology by demonstrating the spend on their technology sensors. Initially, this may involve some administration work by both the insurer and customer, and over time this is likely to become more automated. For example, receipts and invoices may already be scanned into a customer’s accounting software, and in the future, an increasing number of customers will be utilising such technology and the ease of linking to such software is only likely to increase.
A further complicating factor is the accreditation and validation of those sensors. Which ones are any ‘good’ (and cost v quality)? Are those sensors secure? Do the sensors simply monitor or do they allow remote shut-off (say for water leaks)? How many sensors are needed to effectively cover any given size and space? How frequently do the sensors need replacing, new batteries? This learning will come through maturity and will need to be factored into any calculations and pricing.
Improving the Formula
Beyond applying the formula at property and peril levels, there are several other ways that the formula can be quickly improved upon:
Separate calculations by coverage (Buildings, Contents, Business Interruption)
Apply effectiveness weightings to the Expenditure on Qualifying Technology (i.e.h when drawing up a list of qualifying technology the underwriter also allocates the technology a factor to multiply the £ spend by: 0.5, 0.75, 1, 1.5, 2, etc).
It is possible to launch an IoT product, with a premium discount to gain traction, without sacrificing unreasonable levels of pricing inaccuracy or introducing case-underwriting costs. To achieve this, a well-crafted IoT discount function needs interrelating with expert judged parameters.