Revolutionizing Life Insurance: How Data Science, Alternative Data and Technology are Creating Personalized Products.

There is nothing like a good chat with friends, some tapas, and some beers to talk about life insurance, alternative data, data science and the information economics. Some comments I want to share with you.

The first known life insurance policy was issued in the early 1700s, making the life insurance industry a time-honored discipline that has existed for decades. Despite its age, the discipline is primed for innovation, especially with the recent rise of data science and machine learning. In this post, we will look at some potential new IFRS 17 Solvency II and GDPR-compliant life insurance products based on technology and data science (particularly survival analysis).

For those who are not related to insurance and international accounting standards, IFRS 17 will take the place of IFRS 4 as the global accounting standard for insurance contracts. It attempts to improve financial reporting by providing insurers with more transparent and relevant information about their risk exposure, profitability, and financial position. Solvency II is a set of regulatory criteria designed to guarantee that European Union insurers have enough capital to meet their obligations and manage their risks.

Utilization-based insurance is a potential new life insurance product that might be built using technology and data science (UBI). UBI is a kind of insurance that bases premiums on the volume and quality of a policyholder's product or service consumption. In the case of life insurance, UBI might utilize wearable sensors to track the policyholder's health and lifestyle habits, such as eating, exercise, and sleep. This information might then be used to adjust prices based on the policyholder's risk profiles, rewarding healthier lifestyles and providing more personalized coverage.

Another potential insurance product is outcome-based insurance (OBI), which would pay a lump sum to a policyholder or their beneficiaries if certain conditions were met. For example, an OBI policy may be set up to pay out a lump sum if the insured survives a specific illness or reaches a certain age. Massive amounts of data might be evaluated using machine learning to determine the likelihood of meeting payment conditions, and premiums could be modified accordingly.

Microinsurance is a third conceivable product that provides low-income individuals and families with affordable insurance choices. Data science and technology might be used to create microinsurance products tailored to the specific needs of low-income populations, like as coverage for illnesses that are more common in particular areas or agricultural losses caused by climate change. Microinsurance companies may make insurance more accessible and affordable for those who need it the most by leveraging data to create more personalized plans.

Risk management is another use of data science and technology in the life insurance industry. Insurers may use machine learning algorithms to analyze data from a range of sources, such as medical records, social media, and wearable devices, based in survival theory, to more accurately estimate policyholders' risk factors. This might lead to more personalized coverage and more exact pricing, which would benefit both insurers and policyholders.

AI might be utilized to improve customer service in the life insurance sector. Chatbots and virtual assistants might be used to help customers with basic tasks like filing claims or changing policy information, freeing up customer service professionals to focus on more complex issues. AI might also be used to evaluate customer feedback and identify potential for insurance service improvement.

These products not only cover the needs of customers (optimization and frictionless), but also reduce the adverse selection and moral hazard, which are two very common biases in information economics, seen in the insurance industry for over 400 years.

Let me go a little deeper on the first product (UBI) considering the importance of Alternative Data, and Data Science (survival models).

The collection of alternative data from wearable devices, the Internet of Things, and other sources may provide insurers with a wealth of data that can be used to create more customized life insurance products. Smartwatches and fitness trackers, for example, may measure a person's physical activity, sleep patterns, heart rate, and other health-related data. This information may be used to get a better understanding of a policyholder's health and lifestyle habits and to adjust premiums accordingly.

Similarly, IoT technology such as smart home devices, connected automobiles, and even social media may provide insurers with more information about a policyholder's habits, preferences, and risks. Smart home devices, for example, may give information about a policyholder's daily routines, whilst connected automobiles may provide information on driving habits and behaviors. Social media may provide information about a policyholder's interests and activities.

If insurers collect this alternative data, they may use machine learning algorithms (random survival forest, MTLR, and survival deep learning) to analyze it and find patterns and connections. This might help insurers create more accurate risk models and obtain a better grasp of their consumers' needs and preferences. Furthermore, machine learning may aid insurers in detecting high-risk consumers and effectively modifying prices.

Using this information, insurers may create more personalized life insurance products to fit each customer's unique needs and preferences. A policyholder who exercises regularly and has a healthy diet, for example, may be eligible for lower rates than someone who is sedentary and has poor eating habits. Similarly, a policyholder who lives in a high-risk area for a certain sickness may be eligible for coverage tailored to that risk.

Overall, alternative data collection through wearable devices, IoT, and other sources provides insurers with a powerful tool for creating data and building more customized life insurance products. By evaluating this data using machine learning algorithms, insurers may get a better understanding of their policyholders and develop more accurate risk models, leading in better coverage and happier customers.

Life insurance requires innovation, and data science (survival analysis and alternative data) and technology provide several chances to create new and more personalized solutions. Insurers can better detect risk factors and modify premiums by analyzing enormous amounts of data using machine learning. Data science and technology have various potential applications in the life insurance business, including usage- and outcome-based insurance products, microinsurance, and improved customer service. Adopting these technologies and following to IFRS 17, Solvency II, and GDPR standards would allow insurers to provide more relevant and transparent information to their stakeholders and customers, which will benefit both parties.