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Risk Fragmentation: Thoughts on the Next Wave

Risk Fragmentation revolutionates traditional models, transforming insurance risk assessment through granular pricing, integrating Generation AI and quantum computing for enhanced accuracy, and extending coverage to adapt to continuous moments in the lives of non-predetermined assets.

In an era where underlying assets continuously increase in value and the cost of living rises, a more granular approach to pricing becomes imperative. Seamless pricing through Risk Fragmentation not only addresses this need but also creates competitive advantages for insurers. Technology's evolution and widespread adoption further emphasize the urgency for innovative risk assessment models. As the price of data and technology is likely to increase, insurers who embrace Risk Fragmentation can position themselves strategically to navigate this evolving landscape. 

The integration of Generation AI, along with the potential of quantum computing, becomes a cornerstone for improving insurance capabilities. Generation AI brings forth advanced technologies and data analytics, enabling more accurate risk predictions and personalized coverage. Meanwhile, quantum computing enhances processing power, allowing for the development of robust insurance models capable of handling complex risk scenarios

The essence of Risk Fragmentation lies in extending insurance coverage beyond fixed points, embracing continuous moments in the lives of non-predetermined assets or individuals. This shift can be likened to the principles of quantum computing, where conventional computers process binary code (1s and 0s), while quantum computing can delve into the nuances between these extremes. In the insurance context, this translates to a more nuanced and adaptive approach to risk assessment and management.  

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Photo by Michael Dziedzic on Unsplash

In the insurance industry, a transformative shift driven by Risk Fragmentation and granular risk is on the way. This innovative approach involves a meticulous understanding, management, and monitoring of risk within smaller, well-defined subsegments—a departure from the traditional insurance models that focus on protecting specific assets or individuals at predetermined points in time. Technology emerges as the key catalyst poised to unlock a new performance curve, revolutionizing how the industry perceives and mitigates risks

At the core of this emerging era in insurance is the proactive response to one of the industry's most persistent challenges: adverse selection. Asymmetric information has historically contributed to inefficiencies, resulting in the overpricing, or underpricing of policies and inaccurate risk assessments. These inefficiencies contribute to a widening protection gap and an overall decrease in the insurance market. However, new technologies are empowering insurers to reshape the perception of risk, providing efficient protection for customers and clients. This not only addresses the historical inefficiencies but revitalizes the entire insurance industry

In this transformative era of insurance, the integration of Risk Fragmentation and advanced technologies signals a profound shift towards more adaptive, nuanced risk assessment and management. As insurers embrace granular pricing and harness the potential of Generation AI and quantum computing, they stand poised to revolutionize the industry. By proactively addressing challenges such as adverse selection and leveraging technology to provide efficient protection, insurers are not just revitalizing the market—they're reshaping its future landscape, ensuring greater resilience and relevance in an ever-evolving world.

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Photo by Saj Shafique on Unsplash

Examples of Risk Fragmentation Cases: 

Image Recognition for Auto Insurance: 

  • Advanced image recognition technologies are transforming the assessment of auto insurance risks recognizing small pieces of a larger property. 

  • This allows insurers to evaluate and respond to dynamic factors such as road conditions, driver behavior, and potential hazards. 

Voice or Biometrics for Health Insurance: 

  • Utilizing voice and biometric data enables a more personalized approach to health insurance, considering individual health factors and behaviors. 

Vulnerable Audiences for Life Insurance: 

  • Targeting specific vulnerable audiences allows for tailored life insurance solutions, addressing unique risk profiles and needs. 

Usage-Based Insurance (UBI) for Auto: 

  • UBI leverages telematics and data analytics to determine insurance premiums based on actual driving behavior, promoting safer driving habits. 

Climate Risk by Natural Disaster: 

  • Harnessing technology to predict and respond to climate-related risks, including earthquakes, rainfall, wildfires, and floods, mitigates and complements insurance policies. 

Healthy Lifestyle for Health Insurance: 

  • Monitoring and rewarding policyholders for adopting healthy lifestyles using wearable devices and health data positively influences health insurance risk models. 

Smart Home Technology in Property Insurance: 

  • Risk fragmentation in this context involves the integration of smart home technologies like security cameras, smart sensors, and connected devices. These devices continuously monitor and collect data on various aspects such as temperature, humidity, and security breaches.  

Header photo by Umberto on Unsplash

Richard Calvo, Alvaro Takashi Yamashiro, Dian Jin
Richard Calvo
Richard Calvo
Head of Insurtech at NTT DATA Insurance EMEAL
Alvaro  Takashi Yamashiro
Alvaro Takashi Yamashiro
Senior Sector Consultant Strategy & Advisory at NTT DATA EMEAL
Dian Jin
Dian Jin
Sector Consultant Strategy & Advisory at NTT DATA EMEAL
Published on 26/02/2024
~ 4 minutes
Business Transformation