Auto insurance is leading the charge with the adoption of dynamic pricing, which entails developing policies that are more cost-effective for low-risk customers and using a different premium model for high-risk policyholders.
By Ben Burger, commercial and new business executive at Alula Technologies
There is significant potential for life and health insurers to capitalise on this momentum and embrace this model within subsets of their product offerings. These opportunities are born from the ability to tailor premiums based on real-time data and analytics.
Insurers planning to implement dynamic pricing will make use of advanced technologies and sophisticated algorithms through artificial intelligence and machine learning. This will provide an additional layer of insights on top of the massive amounts of data insurers are collecting from various channels. The supporting technology already exists, with the likes of telematics devices, wearables, social media, historical claims records, and health records, which are all being used to provide a comprehensive view of an individual customer.
Understanding this data empowers insurers to gain insights into individual risk profiles and provides them with the means to adjust premiums accordingly. As part of this, we are now able to adapt pricing in real-time, allowing for personalised and dynamic coverage.
This serves to further strengthen the customer experience and promote more frequent interaction between the insurer and the policyholder, building the trust relationship between individual and institution. In turn, this will reduce churn in a highly competitive marketplace.
The importance of data and analytics
Data and analytics are crucial in an insurer’s approach to dynamic pricing. Using advanced analytical techniques, insurers can assess risk with greater precision. This will empower them to better differentiate between high-risk and low-risk policyholders and, per implication, implement more accurate pricing geared to each target market.
Furthermore, data-driven insights provide insurers with a view of emerging trends to guide them in developing products that reflect market needs while enhancing their underwriting practices.
Dynamic pricing also has the benefit of positively impacting customer behaviour, and the relationships insurers have with their customers. With personalised pricing, customers will be incentivised to adopt safer behaviours, such as defensive driving or proactive health management. This shift can lead to a mutually beneficial relationship that sees insurers reward customers for risk mitigation and prevention.
However, some customers may feel uneasy about the perceived intrusion of their privacy or the potential for sudden premium increases. It is, therefore, up to insurers to communicate the benefits of this data usage and address any concerns customers may have to maintain trust.
The challenges of transitioning
Transitioning from traditional pricing models to dynamic pricing poses several challenges for insurers. Accurate data collection, redundant storage, and effective analysis must be maintained. Leveraging reliable data from multiple channels is essential if an insurer is to truly benefit from dynamic pricing.
Additionally, insurers must invest in robust infrastructure, analytics capabilities, and specialised skills to effectively leverage the power of data. Moreover, regulatory considerations, including compliance with fair pricing practices and the use of personal data, need to be addressed to ensure a smooth transition.
Insurers must also be cognisant of the ethical implications tied to dynamic pricing, particularly fairness and discrimination. To address these concerns, insurers must remain committed to maintaining transparency in their pricing methodologies. As such, they must provide clear explanations for premium adjustments based on risk factors and encourage customer education on the benefits of dynamic pricing.
Dynamic pricing provides insurers with the means to fundamentally revolutionise their business models, once proven in subsets of product suites. Integrating data analysis at scale using advanced technologies will enable insurers to better align pricing with risk profiles and customer behaviour. Additionally, customers will be empowered to make proactive choices based on their risk profiles.
There are technical complexities, alleviating customer concerns and addressing the potential ethical implications to ensure a fair and sustainable pricing ecosystem that must all be considered. However, by embracing these and leveraging the opportunities offered by dynamic pricing, insurers can forge stronger relationships with customers, enhance risk management practices, and drive greater customer satisfaction and loyalty.