Do you remember the movie Elysium? Yes, the gigantic space habitat located in Earth’s orbit, which had the medical machine—Med-Bays that cured all diseases, even reversed the aging process by collecting the right data from blood samples. Future has always been a fountain of creative possibilities, for filmmakers and storytellers. Today, their visions have inspired many businesses, like wearable tech; connected devices are a reality. Wearable biometric sensors, such as FitBit can track information related to health and fitness.
As new technologies emerge, consumer trends and preferences change. Similarly, for the traditionally cautious and heavily regulated insurance industry, data technology is transforming their core business model. With the rise of Artificial Intelligence and Machine learning, insurance activities are becoming more automatable. Hence, the biggest opportunity for traditional insures will be to effectively capture and analyze data to better manage financial risks, develop new pricing models and mitigate personal health dangers.
Automation and Predictive Analysis will motivate people to improve their lifestyles, by gathering data from sensors to analyze people’s exercise habits, and vital signs, including their heart rate blood pressure, body temperature and an array of other health metrics. Through rule based algorithms, the accumulated data helps generate information that can recommend behavioral improvements (diet needs, or will suggest for increased activities in daily life)—predictive and prescriptive data and evaluate the results of these recommendations
Artificial Intelligence in Insurance:
The role of AI in logical intelligence is well documented and already being implemented in many industries. What has not been discussed in depth yet is emotional intelligence in relation to AI. This will be an even stronger future driver in the success of an automation/analytics solution for the insurance industry. How? Emotional intelligence can be initially derived from non-verbal gestures, including facial expressions or different tones of voice. Rather than relying on what an individual inputs as their activity (if the activity cannot be recorded automatically through hardware tracking), AI can possibly leverage EQ to ‘see through’ manual inputs of activity to accurately predict the likelihood of the individual successfully accomplishing their tasks. Though research in this area is still quite early, it is promising that insurance organizations are looking towards this direction. It will not only lead to a more efficient insurance industry as a whole but also a much more accurate view into an individual’s health status.
The challenges remain, but we are moving swiftly into a future in the insurance industry and other industries as well where quantifying human health will change the way many organizations operate, especially their interaction with consumers.