The AI-Powered Premium: Personalizing insurance through lifestyle data

The insurance industry, built on the principles of risk assessment and mitigation, is on the cusp of a revolution. Traditionally, insurance premium prices have been based on models that rely on broad generalisations such as age. While risks to the insured vary based on lifestyles and human behaviour, existing models fail to capture the nuances of individual risk.

Artificial intelligence (AI) now offers insurers the ability to assess risk and price premiums with precision, moving away from group-based pricing to an individualised, lifestyle-based approach. This would create a win-win scenario where insurers achieve better risk management, while customers enjoy fairer premiums that reward healthy and responsible behaviour.

Picture this: Martin, a techie and fitness enthusiast, goes to the gym five days a week. He also uses an adjustable desk that allows him to work four hours while standing and walks 10,000 steps a day. He monitors all these activities, including hydrating regularly, on a smart wristwatch. On his phone, he has an app that monitors and records sleeping patterns and screen time.

His colleague, Ken, is the opposite: a couch potato with a penchant for greasy takeout. His love for beer is evident from his protruding belly, and unsurprisingly, he never works out. Why would Martin and Ken pay the same medical or life insurance premiums just because they are within the same age bracket? AI says they shouldn’t, and there are several ways to achieve this.

Firstly, armed with data from wearable devices, including some that measure key parameters like blood sugar levels, AI algorithms can predict risk more accurately than actuarial computations. Additionally, AI could collect data from apps on exercise routines, heart rate variability, sleep quality, and activities such as hikes, runs, and walking distances. This data is directly correlated with health risk levels and probabilities of lifestyle diseases and mortality.

Secondly, AI algorithms are already being used in predicting the risk of diseases, including chronic ones that significantly affect health insurance premiums. Using data on genetics, medical history and lifestyle, algorithms analyse this data to predict the risk of heart attacks, strokes and diabetes.

Further, AI can be used to predict the progression of chronic illnesses like Parkinson’s disease and rheumatoid arthritis. Moreover, even after treatment, AI can analyse patient data after, say, cancer treatment and predict the risk of recurrence. This could be used by insurers to compute risk more accurately.

Thirdly, AI data from connected devices in vehicles can track driving behaviour such as speed, acceleration, braking, mileage and location. This could be used to analyse risk factors for the insured and price the premiums individually. Further, AI can collate data from other sources, such as service history, to have a more realistic risk score.

Lifestyle-based premiums do more than just reflect risk; they actively encourage healthier and safer behaviours. When customers see tangible benefits—like reduced premiums—for making positive choices, they are more likely to adopt and maintain those habits.

For instance, a life insurance policyholder might be motivated to quit smoking, knowing that doing so could lower their premium. Likewise, a driver aware of telematics monitoring may think twice before speeding or engaging in risky behavior on the road.

The writer is a certified member of the Institute of Risk Management (IRM) East Africa Chapter