Camelot held an Executive Breakfast where C-suite executives came together with experts from tech consultancy and engineering company Zühlke, to discuss the power of data.
Digital Twins are elaborate, interoperable virtual representations of objects, processes, or systems. They utilise diverse, real-time datasets to run simulations in parallel to understand the outcome of multiple processes1. The NHS COVID-19 app was designed by Zühlke, a Camelot Corporate Partner, to translate personal health data into epidemiological simulations that provide valuable insight into SARS-CoV-2 transmission. Since its release the app has been downloaded by 22 million users and is estimated to have helped prevent 1 million infections2.
Brief bulleted summary of the topics discussed at the breakfast:
· Using data to evolve the business model · How to move towards loss prevention · How to generate desired consumer behaviour · How to best overcome persistent cultural challenges · The application of the insurance process in the face of climate change · The emerging role of Digital Twins in insurance · The benefits of a data-sharing mindset · How to see through the lens of the consumer · The role of a Chief Data Officer This work inspired an interesting debate:
What if we applied the same scale of data collection used in the NHS COVID-19 app to risk assessment? How can data collection and sharing improve business performance and drive risk prevention strategies?
Hugh Hessing (senior adviser to Insurtech and former CEO of Aviva) shares his views on the ideas discussed.
The Role of Data in Loss Prevention:
While data collection has always been an integral part of the insurance process, Zühlke demonstrated that the way data is currently utilised in the (re)insurance market is only an adumbration of what today’s technology can do.
Digital Twins, Internet of Things (IoT) technology, and enabled devices are capable of measuring and monitoring a huge range of parameters in real time. This data can then be securely shared among devices via the internet and APIs3.
This vast network of data can be exploited for scenario testing through sophisticated data analysis and modeling programs4. The extensive range of possible data parameters allows insurers to consider risks from previously inaccessible angles.
The result of testing is actionable recommendations that aid C-suite insurer decision-making. For example, water escape is one of the most common types of domestic property damage claim. With insurers paying out £1.8 million every day5. Sensors capable of measuring water level, pressure changes, material saturation, and more can monitor changes in real time allowing preventative maintenance to be taken ahead of a leak.
Problems are detected earlier, helping prevent malfunction, minimise disruption, and maximise loss prevention2. As such, risks could be mediated each day and insurers are guided toward effective loss-prevention strategies.
Furthermore, the scenario testing capabilities of this technology provide insurers with the agility to adapt measures in line with changing environmental conditions. Thus, policies can be adjusted in parallel to the changing situation. This means that policies become increasingly detailed and reflect the true perils and hazards of a risk.
These predictions will only become more accurate as the pool of available data grows.
In short, digital twins and IoT technology enable a more comprehensive assessment of risks through the use of data. This data can then be converted into actionable insights that prioritise loss prevention.
Consequently, data could transform current insurance business models by promoting preventative maintenance, generating precise policies, and preventing underinsurance, leading to increased productivity and profitability.
The Evolution of Risk Prevention in a Volatile World:
Traditionally, insurers analyse global trends to diagnosticate predictions for future risk events. However, the modern risk landscape is becoming more volatile. With rising global temperatures, convective processes in the atmosphere are becoming more severe, destructive, and frequent leading to more natural disasters6. Combined with geopolitical unrest and an increasingly complex societal structure, the challenge is advancing by the day.
Consequently, relying on existing global trends is no longer sufficient to provide accurate insights into the possibilities of future events7. To demonstrate the dynamic nature of the modern world, consider that 30 years ago cyber risks rarely required insurance protection. Now, protection from cyber risks is an indispensable part of business policyholders’ insurance.
As it becomes increasingly difficult to use historical scenarios to plan for the future, the way risk assessment is conducted must evolve. As outlined above, a data-led approach can drive loss prevention. Real-time data collection records environmental changes as they occur, meaning predictions for future risks will be based on relevant, up-to-date data metrics. Therefore, predictions and preventative maintenance strategies will be more applicable to the modern risk landscape. Furthermore, better digital analytics is a pre-requisite for a comprehensive appreciation of the pervasive impacts of climate change on risks8.
As Hugh summarised, we are living in a turbulent time when traditional practices are racing against climate change. Embracing data as an integral part of (re)insurance business models can allow individuals to take their foot off the gas.
Interpersonal Challenges to a Data-Lead (Re)Insurance Market:
The most prevalent barrier to the adoption of a “prevention not prediction” data-led business model is an interpersonal one. Insurance is a market notoriously resistant to change and the current method of risk prediction has been stable for decades; if something’s not broken, why fix it? However, in the face of a volatile modern world, this methodology is quickly becoming obsolete.
Implementing a data-focused business plan for risk assessment and loss prevention is a practical solution to help modernise the industry. However, Hugh believes that data collection on this scale is a complex, expensive process and that insurers could be forgiven for being nervous to take on the data collection and accompanying responsibilities. C-suite executives will be faced with the massive challenge of putting aside enough investment to build an effective digital architecture to collate and manage data while keeping the business operating smoothly during the transition.
Despite this, the potential for these technologies is gradually being recognised. For example, appointing a Chief Data Officer will not eradicate the complexity of the approach, but a recognition that a position like this would streamline the transition process is a great first step!
Furthermore, as technology continues to advance, this process will become increasingly accessible through fluid IoT and API integration.
Recognising the power of data will provide insurers with a competitive edge. Conversely, clients who embrace data will become more attractive to insurers; attritional risks are managed by the client, and the insurer can provide practical protection when the catastrophe hits.
The application of this technology to risk assessment is only in its embryonic stage, yet Hugh is hopeful that it will only take one major insurer to embrace the challenge and the rest of the market will follow.
For a deep dive into the emerging topic of digital twins and its impact on the insurance sector, check out Zühlke’s recent article: Know Thy Risk: The Emergence of Digital Twins and the Reimagination of Insurance.
4: Klein, D., (2023) Digital twin examples: public health during COVID-19 (zuehlke.com)
5: Association of British Insurers., Burst pipes and water leaks | ABI
6: Charpentier, A., (2007) [PDF] Insurability of Climate Risks | Semantic Scholar