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Predictive analytics in life insurance: everything you need to know

Predictive analytics in life insurance uses statistical techniques and algorithms (created by data scientists, historical data and outcomes) to make predictions about future events.

Today, predictive analytics are integral to the life insurance industry. They're used for pricing models and risk selection, claims processing, fraud detection and more.

The rise of predictive analytics

Actuarial science started in the early 20th century. Its significance grew exponentially due to the growth of digital technology and big data over the past 30 years. Sophisticated algorithms were developed in the 1980s. Then came the rise of machine learning (ML) and artificial intelligence (AI) in the 2000s.

These technological advancements revolutionized the sector by powering more accurate underwriting and personalized insurance products. Advanced computing capabilities let us process vast amounts of data at unprecedented speeds.

It begins with the data set

Predictive analytics in life insurance

Predictive analytics has become a cornerstone of the life insurance industry, profoundly transforming how insurers assess risks, set prices and tailor policies. Leveraging vast data sets and sophisticated algorithms offers a nuanced understanding of risk factors, leading to more accurate and individualized assessments. Life insurers are using predictive analytics to improve three facets of their operations:

Risk assessment or underwriting

Historically, life insurers assessed risk based on cohorts associated with age, medical history, lab results and lifestyle choices. Predictive analytics incorporates a wide range of variables—including genetic information, social habits and environmental factors—to create a more comprehensive risk profile of each applicant. With this detailed analysis, insurers can more accurately identify potential risk factors and do it exponentially faster.

Advancements in predictive analytics have revolutionized automated and accelerated underwriting, resulting in more efficient, accurate and faster risk assessments. As the industry is still settling on consistent definitions, the impact is undeniable. We can now analyze large volumes of data to identify patterns and correlations that would be impossible for mere mortals to identify.

Pricing

Predictive analytics enables insurers to move beyond a one-size-fits-all model to more dynamic, usage-based pricing. Insurers can set premiums more accurately, reflecting an individual risk profile. This approach not only ensures fairer pricing for policyholders but also helps insurers manage their risk portfolios more effectively.

Personalization

While not yet mainstream, predictive analytics can support personalized life insurance policies. Insurers can offer coverage that aligns closely with individual needs and preferences. This customization ranges from flexible coverage options to rewards for healthy behaviors, creating a more engaging and relevant product for policyholders.

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Benefits for both insurers and policyholders

Integrating predictive analytics in life insurance is a win-win for insurers and policyholders. The shift toward a more data-driven, personalized approach heralds a new era in which accuracy, efficiency and “customer-centricity” are attainable.

For insurers, the benefits of predictive analytics are many. Improved risk management is a key advantage. By accurately predicting which policyholders are more likely to make claims, insurers can adjust their premiums accordingly, ensuring a balanced risk pool. This accuracy in risk assessment translates into cost efficiency, as insurers are better equipped to allocate resources and manage claims. Plus, predictive analytics aids in fraud detection, resulting in significant savings for insurance companies.

Policyholders also reap substantial benefits. Fairer premiums are a primary advantage, as individuals are charged rates that accurately reflect their personal risk rather than a generalized average. This fairness in pricing, especially for those with lower risk profiles, enhances the value proposition of life insurance.

Furthermore, policyholders experience faster, more efficient service, as predictive analytics streamlines the underwriting process. Claim processing becomes quicker, and customer service improves due to the insights gained from data analytics.

Building a predictive model

In the life insurance sector, predictive models play a pivotal role in analyzing data and forecasting future trends. These models leverage data, ML and AI to make informed predictions about risks, policyholder behavior and claim probabilities. Effective predictive analytics are impossible without massive quantities of historical data and, most importantly, outcomes. Data scientists, working in collaboration with underwriters and actuaries, use these components to build, train and test predictive models.

The algorithm that changed it all

The impact of predictive analytics

By harnessing advanced algorithms and ML techniques, insurers can analyze large volumes of data to identify patterns and correlations that might not be evident through conventional methods. Predictive analytics:

  1. Helps insurers create an accurate risk assessment, ensuring that premiums are more closely aligned with the actual risk.
  2. Speeds up the underwriting process, resulting in a more efficient and customer-friendly experience. This efficiency not only benefits customers, who enjoy faster service and potentially lower costs, but also insurers, who can process more applications with greater precision.
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LifeScore Labs’ predictive solutions

The industry is working towards making the application process more accessible, accurate and customer friendly. That's why our goal is to revolutionize underwriting with advanced analytics and data-driven risk assessment. Our solutions include LifeScore Med360 which assesses risk using data from the application, family history and clinical labs. LifeScore EHD360 leverages data from electronic health records and other data sources, providing risk assessments on the most up-to-date information available.

What tomorrow holds in store

It is not a stretch of the imagination to say that using predictive analytics in the life insurance industry will only increase. Several key factors are set to shape this landscape as we look ahead. They include:

Greater adoption of AI and ML

The use of AI and ML will continue to grow within life insurance, leading to more accurate risk assessment. These capabilities will help us to better analyze large data sets and uncover nuanced insights about individual risk profiles.

Alternate data sources

The industry will increasingly leverage new data sources such as wearable technology, social media and even climate data. We’re also keeping a close eye on the development of the Trusted Exchange Framework and Common Agreement (TEFCA) network. TEFCA has the potential to impact the power dynamics of data acquisition.

Dynamic pricing models

Real-time data and predictive analytics could pave the way for dynamic pricing models that adjust premiums based on ongoing health data or lifestyle changes, offering incentives for healthy behaviors.

Ethical and regulatory issues

Ensuring data governance and privacy in AI models will be paramount. We’ve argued that having a principle-based governance program in place puts human values at the center of technological development.

As predictive analytics evolve, it will continue to make the entire process more personalized, efficient and aligned with individual needs.

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