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Predicting the propagation of risk linked to human behavior

The Spatial Risk Diffusion Model can allow insurers to gain a competitive advantage in risk assessment.

Insurers that do not look beyond their traditional data sources and analytical methods will be left behind by the industry’s digital disrupters, as well as current competitors that move out of their comfort zones and adopt modern risk assessment measures.

The Spatial Risk Diffusion Model is one tool that would allow insurers to gain a competitive advantage in risk assessment. With it, insurers could see the impact that human behavior has on the progression of risk throughout society.

At the heart of this model is the notion of risk, as a social construct, adopted from theories of social constructivism, combined with network diffusion models. The premise is that societal norms, which determine the level of risk taking, are formed through the social network interactions of a group.

Those societal norms then influence the behavior of an individual within the same group. The theory of network propagation of risky behaviors has been supported in the past by researchers—for example, in a medical study out of Harvard Medical School on the spread of obesity among friends and studies out of Harvard and the University of California-San Diego on smoking cessation.

The insurance industry, however, has not applied the theory to understand the propagation of risk trends throughout the larger population. To improve the existing theoretical foundation, Accenture partnered with the Stevens Institute’s Complex Evolving Networked Systems Lab.

In a nearly three-month-long project, the research team studied 12 years of National Highway Transportation Safety Administration (NHTSA) data on risky teenage driving behavior, as measured by fatal and severe non-fatal accidents.

Applying the model, the researchers predicted with meaningful certainty which statewide populations of teen drivers would be led by the behavior of young drivers in other states, as well as the time needed for that behavior to propagate. For example, the model accurately predicted that the safer driving behavior of teens in North Dakota would lag behind the same behavior of teen drivers in Maine by more than a year.

Other insurable risks driven by human behavior that could be assessed better with the model include employers’ liability, burglary and health, particularly health risks related to smoking and obesity. Carriers would be able to identify which population groups influence others and how long that influence takes to propagate.

With this advanced understanding of how risk progresses throughout society, insurers would improve their positioning against new market entrants, as well as current competitors, and continue to grow their business.

In addition, they would have a competitive edge in assessing whether they should enter, exit or remain in a geographical marketplace. They could, where regulation allows, use the model to develop rates. Insurers that apply a Spatial Risk Diffusion Model would also likely realize benefits in loss reserving.

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