Wearable Tech and Health Insurance Actuarial Insights: Revolutionizing Risk Assessment in Healthcare

It is clear as daylight that health insurers cannot just keep increasing premiums every year as it has reached an affordability breaking point worldwide. The focus should now be on preventive side so that claims propensity and severity is reduced over time. Wearables allow big data to be generated in the first place (generating big data from just age, gender, zip code socio-economic data is not possible; that is just small data), which can then be analyzed by upgraded AI and ML modeling methods by actuaries rather than using outdated models on small data for pricing and analytics. Integrating wearables with wellness product rewards can nudge people towards healthier behavior and show that via frequent touchpoints with customers that insurer is your partner in good times in daily life too instead of coming to your aid only in the rare bad times. Majority of risk is determined by lifestyle behavior in health and driving behavior in motor and that can be captured through wearables instead of relying only on averages like age, gender, relationships and occupancy.

Wearable technology has undergone a significant transformation in recent years. Initially focused on basic metrics like steps taken and calories burned, these devices now track a wide array of health indicators, including heart rate variability, sleep patterns, and even blood oxygen levels. This evolution has turned wearable tech into a potent tool for continuous health monitoring, offering real-time data that was once only accessible through medical examinations.

Actuaries, as professionals who analyze financial risks using mathematics, statistics, and financial theory, are finding wearable tech data invaluable. This new wave of detailed, personalized health data enables actuaries to refine their risk assessment models, moving beyond broad demographic factors like age and gender to include dynamic health metrics. This shift not only improves the accuracy of risk predictions but also allows for more nuanced understanding of individual policyholders' health trajectories.

The influx of data from wearable devices is paving the way for personalized health insurance policies. By analyzing data from these devices, insurers can tailor premiums and coverage to individual risk profiles. This approach not only benefits insurers by reducing uncertainty but also incentivizes policyholders to maintain healthier lifestyles, as improved health metrics could lead to lower premiums.

The integration of wearable tech into health insurance raises important ethical and privacy concerns. Questions about data ownership, consent, and the potential for discrimination based on health data are at the forefront. Insurers and policymakers must navigate these issues carefully, ensuring that the use of wearable tech data upholds high ethical standards and respects individual privacy.

As wearable technology continues to advance, its role in health insurance is poised to expand. Future devices may be capable of monitoring a broader range of health indicators, further enhancing the accuracy of actuarial models. However, this progress also brings challenges, including the need for advanced data analytics capabilities, addressing potential biases in data, and managing the ever-growing concerns around data privacy. Big data is rapidly entering into actuarial field but the more the data, the more the room for misinterpretation or biased interpretation. There is indeed more reason in data than in most of our opinions. We also have to realize here what the Nobel laureate Ronald Coase says ‘torture the data long enough, and it will confess anything’. Almost any opinion can be defended through selected use of the massive data sets now.

That is not to say that analysis on small data is redundant or maxed out. Even in traditional conventional areas there is a lot of analysis that can be done on health data. For instance, in our experience of undertaking health reserving, data helps to reveal important trends on to the surface. For lag between paid and loss dates, there seem to be a number of factors operating simultaneously. For instance, retail clients can take more time to inform insurers of claims for reimbursement than professional hospitals with vigilant administration teams. However, hospitals also treat more complex illnesses and this can lead to greater time for a claim to be eventually paid off. Retail clients have higher propensity for Incurred But Not Reported (IBNR) Claims while hospitals have higher propensity for Incurred but Not Enough Reported (IBNER) Claims. There are also significant cross-over effects at work here; for instance, for outpatient clients if health worsens, they get referred to hospitalization.

Data can also highlight important features of claims administration. For instance, retail clients are paid lower proportion of billed amount relative to hospitals. This might be due to lesser documentation from clients or unstructured procedure for handling the situation leading to lower proportion being paid than hospitals with professional teams.

Analyzing say top 10 claimants can lead to understanding of concentration risk on clients. Data also reveals crucial insight for network risks.

Moving on, Vitality is the most prevalent and pioneer example of wellness in life and health insurance. The Big Data Case Study shows the impact of wellness on morbidity and mortality in detailed manner as follows :

• Average claims of a medical scheme increase by 2.5% for every year that the average age of a medical scheme increases. Vitality attracts younger demographic more and so their average age of portfolio is 1.5 years younger than the industry, resulting in claims savings of 3.7%.

• People that start at a high level of exercise have 17% lower hospital costs than those unengaged. People that start at a low level of engagement and increase exercise reduce their hospital costs by 14%.

• People that start at a high level of exercise have 55% lower mortality than those unengaged. People that start at a low level of engagement and increase exercise reduce their mortality by 69%.

In summary, the integration of wearable tech into the realm of health insurance actuarial insights marks a significant shift in the industry. It offers a more personalized, dynamic approach to risk assessment and policy design, benefiting both insurers and policyholders. However, as this field evolves, it is imperative to address the ethical and privacy challenges that accompany the use of personal health data. By striking a balance between innovation and responsibility, wearable tech can continue to revolutionize health insurance, making it more responsive to individual needs and contributing to a healthier society.