Insurance is shifting. Legacy models built on age, location, and past claims are no longer sufficient in a world where risk is dynamic and customer expectations are rising. Behavioral analytics in insurance is creating new opportunities for carriers to operate with more accuracy, speed, and precision by using real-time data to understand how people actually live, move, and engage.

Across underwriting, insurance pricing, fraud detection, and customer engagement, behavioral analytics is helping insurers move from assumptions to insights. As new technologies emerge and the regulatory landscape evolves, this is becoming a core capability for innovation and data leaders across the industry.

Understanding behavioral analytics in insurance

Behavioral analytics refers to the use of data derived from individual behavior to inform business decisions. In insurance, that includes driving patterns, mobility habits, lifestyle data, digital application behavior, and even signals from the open web and social networks. Unlike static demographic data, these behavioral signals evolve constantly. They offer a closer view of risk and help carriers understand customer needs with more granularity.

This translates into measurable business value. 

 Usage-based insurance programs like Progressive Snapshot have collected billions of miles in driving data and offer up to 30 percent discounts based on safe driving. Reviews suggest these programs are influencing driver behavior and policyholder expectations.

Personalization is also delivering business outcomes. Personalization in insurance can lift revenues by 10 to 15 percent and increase customer retention by up to 20 percent.

Underwriting becomes more precise when pricing is tied to how someone drives rather than a proxy like ZIP code and demographic data. Fraud detection improves when insurers can recognize patterns of intent based on how an application is completed. Behavioral biometrics such as typing cadence and hesitation patterns are already being used to detect inconsistencies and prevent false claims. Customer engagement becomes more effective when outreach is informed by recent life events rather than generic renewal cycles.

Behavioral analytics also enables proactive risk management, allowing insurers to intervene before a loss occurs. For example, behavioral health insights can be used to support well-being programs or preventive care in life and health insurance.

Finally, behavioral data unlocks timely cross-selling opportunities, as it reveals not only what a customer needs now, but what they might need next. Behavioral indicators, like researching real estate, travel planning, or fitness tracking, offer insurers a real-time window into evolving customer journeys, enabling smarter bundling and new product offerings that reflect actual intent.

How the technology works

Several new approaches are making this level of insight possible. Connected devices such as smartphones, wearables, and telematics systems generate continuous data on mobility, activity, and interaction. Artificial intelligence systems process this information and surface patterns that signal risk, intent, or opportunity.

One company applying this in a real-world context is Sentiance. Their platform turns mobile sensor data into lifestyle insights, helping insurers understand daily routines, levels of physical activity, and commuting patterns. These insights allow for more personalized pricing models and can support proactive risk prevention.

ForMotiv focuses on digital behavioral intelligence. By analyzing how users interact with forms and applications such as mouse movement, typing cadence, and hesitation, they provide real-time insight into the likelihood of fraud or policy abandonment.

Socalytix offers another layer of intelligence by analyzing digital footprints and public online data to detect life events. These events such as marriage, relocation, or a new job can be early indicators of changing insurance needs. By acting on this information, insurers can offer more timely and relevant products while also validating information during onboarding or claims review.

These companies are already working with insurers to increase efficiency, improve loss ratios, and boost customer satisfaction. Their success reflects a broader shift in how the industry defines and manages risk.

Why this matters now

Consumer expectations have changed. A Capco survey found that 89 percent of U.S. policyholders are willing to share personal data in exchange for more personalized offerings. This trend is consistent across global markets and especially strong among younger customers.

At the same time, regulators in several U.S. states - including California, Hawaii, Massachusetts, and Michigan - are either banning or limiting the use of credit-based insurance scores (Experian). Behavioral analytics provides a viable alternative, offering insurers a more accurate and equitable way to assess risk using real-life indicators.

The volume of available data is also growing. Connected devices, mobile usage, and digital applications are generating behavioral signals at scale. Innovation teams now face the challenge and the opportunity of turning that data into insight.

What comes next for innovation leaders

For decision-makers in insurance, the opportunity lies in moving from pilot to practice. Behavioral analytics is no longer experimental. It is a proven lever for improving accuracy, reducing fraud, and creating better customer experiences. The key is to identify the right use cases, deploy trusted technologies, and ensure internal teams are equipped to work with new data sources.

This also means building a strategy that includes strong data governance, privacy protections, and cross-functional collaboration. Underwriting, claims, digital, and data teams all need to work from the same source of truth. When aligned, these capabilities enable insurers to operate faster, smarter, and with greater confidence in the decisions they make.

How SOSA helps companies lead in insurance

At SOSA, we help insurers identify and implement high-impact technologies that align with their business goals. Our team scouts and validates behavioral analytics startups that enable pricing precision through real-time behavioral risk indicators, detecting fraud through intent-based user signals, preventing losses with predictive insights, and driving personalized engagement and timely cross-selling based on lifestyle shifts and digital behavior. By matching insurers with best-fit solutions and guiding the pilot and implementation process, we help accelerate time-to-value while reducing risk. Through our work with carriers around the world, we have seen how behavioral analytics can shift insurance from reactive to proactive, and we are here to help make that shift both practical and measurable.