This blog was originally published on InsurTech Gateway by Hambro Perks, click here for the original blog.
It is a fascinating time to work in the risk analytics business.
Traditional risks are changing, with much of this change being driven by technology. From the challenges posed by autonomous vehicles to the rapid digitization of the “smart home”, with automatic detection of threats such as fire and theft, systems are getting smarter and risks are changing.
Other types of traditional risk however still offer tremendous opportunities — last year’s storms in the U.S. have shown that even in one of the world’s most established insurance markets, uninsured losses are still a major problem. Barely half of the losses from Harvey, Irma and Maria were insured — the rest lies with the uninsured victims of the disaster, or if they are lucky, with the federal government who will help them rebuild.
This “protection gap” between those who have adequate insurance and those who do not, represents both a huge societal challenge, and a massive opportunity for the insurance market.
For many retail consumers, insurers have been progressively moving away from what has historically been a customer experience characterized by infrequent and adversarial interaction, toward becoming trusted advisors; tailoring products to individual customer needs and offering advice and resources to avoid damage. Insurers are moving from a traditional role of indemnification to one of shared responsibility.
Technology is also having an influence on how insurers respond to consumers. This can easily be seen around the way that insurance is bought. You are no longer asked whether your front door lock complies with a specific lock specification or standard (for readers in the U.K, the BS3621, for instance), you just select your door lock from one of the images shown. It is all about the customer now, and making their experience better, letting consumers see directly how much their premium might fall if they fit a better lock. It benefits everyone to increase resilience.
Will insurers instruct locksmiths to fit better locks ahead of any burglary? Image: Pixabay
So too in the broader property and casualty (P&C) industry, insurers and reinsurers are looking for ways to deliver more valuable products. The notion that insurers settle claims, but also help reduce the potential for claims, is a powerful one. At RMS, we provide the analytics that insurers use to price risk. We are increasingly seeing our analytics used to understand the benefits that can be had by taking action to reduce risk.
Reducing the likelihood of claims is of fundamental benefit to everyone, and we often talk about the concept of the “resilience dividend” — the conceptual reduction in premium that can result from taking a measure that reduces risk.
This is a hot topic in the public sector right now — governments, both national and local, are striving to allocate budgets most effectively, getting maximum “bang for their buck.” The aim is to reduce risk whilst offering financial protection against disaster, therefore providing a dual benefit to insurance protection. We know for example that in low-middle income countries, that for every US$1 spent in pre-disaster financing (insurance), at least US$4 in disaster relief funding is saved. But more than this — that US$1 insurance spend could actually be channeled into resilience financing — providing both risk reduction and post-event recovery funding.
If your insurance protection can also be designed to reduce risk in the first place, surely that is best for everyone. Just as the “little black box” in the car encourages safer driving to reduce premium, so too the resilience and risk reduction can be structured into huge public financing decisions. If we retrofit buildings against earthquake, we reduce risk, and ultimately premium should fall. The virtuous circle is completed in that these savings can then go towards the retrofitting expense.
Within this approach, InsureTech firms are helping to drive change. Analytics is moving from two-stage risk assessment — beforehand to price, afterwards to settle claims — to a full risk life cycle assessment. This involves responding to changes in risk as preventative measures are taken and the situation on the ground evolves. For example, a parametric approach to flood insurance as offered by FloodFlash enables a consumer to choose exactly where their flood insurance protections kick in — whether that is at a low-level flood that disrupts business, or a higher level of water that causes more physical damage. That protection need is also likely to vary as insureds take mitigation measures, which could range from physical infrastructure such as sand bags or flood barriers, or to moving the contents to the second floor when it starts to look bad, through to fitting electrical points higher up a wall to avoid flood damage.
The analytics needed to support these sorts of ideas in the property catastrophe world are highly sophisticated, to understand the impact of an individual flood risk protection measure, we need detailed, highly dependable analytics. Modeling U.S. or Europe flood requires very high-resolution data, across domains of thousands of miles. The range of possibilities also requires incredibly dense stochastic event sets to understand tail risk. The result is a computational demand that can only be met by big data solutions, deployed at scale in the cloud.
Accessibility to these analytics is on the rise, and quickly. New start-ups can get the benefits of incredibly sophisticated risk analytics with a web browser or a few API calls.
These improvements in efficiency help drive the affordability of insurance, spurring competition, and ultimately driving down cost for the insured. Insurance is an industry where there is ample room for improvement — in today’s London insurance market for example, some 40 percent of the cost of insurance goes to covering business costs and administrative burden, rather than covering risk.
And so this symbiosis continues — insurance products (and channels) evolve, supported by evermore sophisticated analytics, but also driving the demand for these analytics, and pushing the boundaries at every step.
Will insurers one day send someone round to fit new locks before a break-in rather than after? Maybe. Will locksmiths provide bundled insurance, disrupting the insurance value chain delivering protection alongside physical product? Potentially. Will insurers embrace the opportunity and partner with the experts in their field to figure out the most effective risk reduction measures and deliver this value to consumers? Definitely — and it is already happening.
Consumers and the insurance market as a whole, are demanding higher value products, greater convenience, and greater resilience. Insurance buyers want more impact from their purchasing power. Technology, and especially the InsureTech sector, is pushing the boundaries of what is possible. It really is a fascinating time to work in the risk analytics business.
As head of Moody's Consulting Services team, Ben oversees the services work that helps customers maximize the benefits of working with Moody's RMS models, data, and applications. He leads a multi-disciplinary services team, comprising nearly 50 individuals with a combined market experience of more than 200 years.
Ben's leadership role covers all areas of the market, from implementation and technical consulting to on-board and deploy Moody's solutions, to process and workflow engagements that help drive the significant business benefits that flow from a robust view of risk, and through to the quantification of risk to support risk transfer, including innovative forms of risk packaging.
During his 16 years with Moody's, Ben has led numerous services teams, and business development for establishing best practices in new markets for the business, such as public sector and broad financial services. Ben is closely involved with the significant investments that Moody's makes in climate change. Ben holds an MEng in engineering mathematics from the University of Bristol.
