The future of dynamic pricing for insurers and customers
Consumer demand for convenience is growing by the day, with many expecting personalised insurance policies from their carriers, delivered at the point of need, and tailor-made for them. The reality facing insurers is that rating mechanisms driven by legacy software are no longer adequate, with mounting pressure to explore new avenues to deliver seamless experiences.
Positively, however, the digitalisation of insurance is continuously creating new avenues to deliver efficiencies – both internally and for the end consumer. Investing in innovative technologies, including improved data and analytics capabilities, allows businesses to cater to evolving demands, drive real value, and improve operational agility.
The evolution of these capabilities means that insurers now have the tools they need to deliver policies that are tailored toward the risk profiles of individual customers and can be adapted in real-time as circumstances change. Driving habits, for instance, can be reflected in the cost of auto insurance thanks to telematics data captured by an onboard ‘black box’. Meanwhile, in the field of corporate insurance the deterioration of boat hulls based on routes traversed and weather conditions can be examined to inform marine hull insurance.
This is known as dynamic pricing.
What is dynamic pricing?
Dynamic pricing is commonly used by airlines and hotels to balance supply and demand, with ride-sharing services like Uber recently bringing the model into public discourse. Yet the proliferation of advanced technology within previously paper-based sectors means that industries like insurance can leverage the same principles to better serve their customers.
Dynamic pricing enables insurers to respond and act upon real-time market changes, in alignment with consumers’ expectations. With the right technology and capabilities, insurers are able to deploy prices quickly and in a way that reflects those expectations.
As a result, cheaper policies can be offered to low-risk customers, while high-risk policyholders will have a different premium model. This involves assessing how much risk a customer truly represents by factoring in things like usage and risk-averse behaviours, and allowing for pricing to evolve against circumstantial shifts.
Traditionally, the process of gathering data to deploy rates takes several months, largely because of the nature of legacy systems. Changes in the market take time to filter into policies, meaning these are inefficiently delivered to consumers.
Particularly against the backdrop of rising inflation and the cost-of-living crisis, consumers facing financial constraints and changing personal circumstances require timely offers. Providing outdated rates based on old data runs the risk of consumers turning to insurers that can provide a smoother service and more competitive options, especially as the cost of switching insurance products and carriers has declined massively in recent years, enabled in part by the rise of comparison websites.
Embracing true digital transformation in insurance means adopting a shift towards dynamic pricing and sophisticated technologies like AI. Adopting AI-driven pricing will allow insurers to maintain relevance and viability in an ever-changing market.
Services such as instant quotes have been a feature of insurance policies for some time, however as pricing algorithms mature and connected telematics and Internet of Things (IoT) devices continue to proliferate, the explosion of data points available to individual customers will ensure that these are carefully tailored to each policyholder.
Unlike traditional rules-based engines which cannot adjust for unforeseen events, sophisticated platforms can leverage decision-making tools such as “what-if” scenarios, enabling insurers to simulate data-driven scenarios that predict pricing outcomes with the highest chances of success. Millions of rates and product options can be calculated each day thanks to analytics-driven solutions, which pull and synthesise insights from day-to-day behaviours, enabling businesses to scale with ease while also offering consumers more of the convenience and personalisation they crave.
While the initial upfront investment of time and resources might deter some insurance companies from implementing a dynamic pricing system, in reality, these solutions are often more cost-effective than legacy systems, particularly in the long term.
Single, end-to-end insurance platforms that combine iterative deployment with ratemaking and execution capabilities give insurers a competitive advantage, even during challenging market conditions. The ability to respond in real-time to changes in consumer preferences and behaviours, while ensuring compliance and governance over the rate-making process, allows businesses to realise value almost immediately.
What stands truly advanced systems apart from their competitors is the ability to personalise product offerings based on granular insights.
Consumer needs and behaviours are different from what they were 20 or even 2 years ago. For example, some prefer to pay for insurance based on their driving habits, rather than paying for a one-size-fits-all generic rate. Indeed, a survey by the IBM Institute for Business Value (IBV) revealed that 50% of customers prefer tailor-made products, signalling to insurers that the expiry date for outdated systems is fast approaching.
Dynamic usage-based insurance (UBI) models, which can capture and analyse vast amounts of telematics data, allow insurers to transition away from standardised purchase and annual renewal models to a continuous cycle whereby offerings are constantly adapted to an individual’s behavioural patterns. They are also enabling the growth of the sharing economy, offering the ability to pay-by-mile for car sharing or pay-by-stay for home-sharing services.
The number of data points and signals that dynamic pricing models can utilise to generate tailored rates is vast. In mobility insurance, technologies could assess everything from a driver’s adherence to speed restrictions to kilometres travelled and how frequently they switch lanes. Meanwhile, automated price adjustments can be made more quickly and easily based on current data and situations, unlike the highly manual and time-consuming undertaking associated with traditional models. Hence, insurers are enabled to rapidly react on market developments and competitor pricing.
The future of dynamic pricing
Dynamic pricing is levelling up in line with accelerating digital adoption. The rise of self-driving cars, for instance, is a catalyst for hyper-personalised, real-time auto insurance plans.
Picture this scenario: a driver takes their car out of self-driving mode and maps out a potential route on their GPS, which is shared with their mobility insurer via their smartwatch personal assistant. They are immediately steered towards an alternative route that has a much lower likelihood of accidents and auto damage due to the distribution of cars on the road. Upon accepting the suggestion, the driver is notified that his mobility insurance premium has dropped a few points, while his life insurance premium has decreased as well to account for lower levels of risk.
The technologies that enable these interconnected ecosystems already exist, and many are available to customers. As AI and big data become more deeply integrated into everyday life through connected devices, whether that is smart watches, home assistants, or self-driving cars, individuals will see the benefits of predictive analytics and the vast amount of data points that will reshape claims, underwriting, and pricing.
For insurers, the best defence against falling behind rapid sector-wide changes is to get on the offensive, find the best technology solution that is compatible with enterprise-wide systems, and implement advanced pricing software that can evolve with customer needs.
About the author: Anja Friedrich is the Associate Partner of Synpulse, a global management consulting company and partner to financial services providers. Sympulse solves challenges and helps businesses evolve to capture the potential of the new economy.