March 22, 2016 by Craig Harris, Freelance Writer
The field of behavioural economics could have potential applications in insurance claims, fraud deterrence, injury management, driver safety and client service, some experts contend.
Led by academics such as Richard Thaler, Cass Sunstein and Dan Ariely, behavioural economics holds that psychological bias or irrationality often pushes people to make decisions contrary to their best interests. The concept of nudging is based on research that shows it is possible to guide e to socially ethical or utilitarian ends by presenting choices in different, often simplified ways.
“Nudge theory” in particular was popularized in the 2008 book, Nudge: Improving Decisions About Health, Wealth, and Happiness by Thaler and Sunstein. The book explores the potential for behavioural economics to improve the effectiveness of government (and other) programs by better understanding social norms and human behaviour, especially related to ethics in the decision-making process.
Far from an abstract theory, nudge has been put into practice in government and policy settings around the world (interestingly, Sunstein was Administrator of the U.S. White House Office of Information and Regulatory Affairs from 2009-2012).
The concept really took hold in the United Kingdom in 2010 when Prime Minister David Cameron set up a Behavioural Insights Team (BIT), dubbed the “nudge unit.” The team examined a wide range of policies in diverse areas-everything from taxation to pensions to police diversity-before it was spun off from government in 2014 to become a private company. More recently, the U.S. government formed a nudge unit called the Social and Behavioural Sciences Team.
One interesting project from BIT involved testing whether a new tax reminder letter to recipients informing them that most of their neighbours had already paid (social norm) would increase payments. The unit claimed that it nudged forward £30 million ($62 million) per year in income tax for HM Revenue and Customs.
“A number of governments are setting up think tanks to specifically focus on behavioural science,” notes Keith Walter, joint leader of the actuarial, reward and analytics team at Deloitte in Canada. “It is a huge public policy issue and a big theme right now in government in Canada, particularly regarding evidence-based decision making linked with behavioural insights.”
So what does this have to do with property and casualty insurance? Potentially, a great deal, according to Deloitte Consulting’s chief data scientist, James Guszcza.
“Advertising has effectively done this behavioural nudging for years, and now you are seeing its use for more efficient government, better policies and improved social outcomes,” he says. “I think nudges can be used in other pro-social ways, and the insurance claims process is a great example.”
For instance, Deloitte worked with the unemployment insurance agency of a U.S. state to test low-cost methods of identifying and curbing fraud amongst claimants. Deloitte did not release the name of the state. Guszcza notes that his firm used sophisticated analytics capabilities-including predictive modeling, machine learning, anomaly detections, and analysis of behavioral patterns-to identify likely abuse early so preventative action could be taken.
Coupling predictive analytics with behavioral nudge tactics, claimants were given helpful information at key moments in the application and certification process. The UI agency has seen substantial improvement in the accuracy of initial claim filings and weekly certifications, according to Guszcza.
“It was a ‘Eureka’ moment for us that showed this actually can and does work,” he says. “We have floated these and other results to insurance companies and the interest has been very strong. There is little downside risk to experimenting and there is a lot of upside potential that has a positive social effect.”
An obvious example in p&c insurance is fraud detection, especially the “soft” or opportunistic variety (honesty-prompting nudges would have little effect on organized, premeditated activity, Guszcza notes).
“The issue around opportunistic fraud is one that gets people’s attention,” says Walter. “There is a general recognition that bringing behavioural science into managing the claims process around soft fraud is a key issue. There is a lot of interest in how you actually influence behaviour so that the claimant is less likely to exaggerate or pad the claim.”
Nudge tactics “offer a ‘soft touch’ approach that is well suited to the ambiguous nature of much fraud detection work,” Guszcza notes.
There are extrinsic or external factors involved in soft fraud, which can be addressed through what Guszcza calls “judiciously worded letters” that include specific details about the claim and remind claimants about fraud detection policies. Letters could also refer to random or “lottery” fraud investigations that may have a “sentinel effect” on exaggeration of embellishment, according to Guszcza.
However, insurers could also appeal to the individual’s intrinsic reward system as well. Research in behavioural economics suggests that a small amount of cheating flies beneath the radar of people’s internal ethical codes, according to Guszcza. Behavioural economist Dan Ariely coined the term “personal fudge factor” to describe this tendency.
“Such nudge tactics as priming people to think about ethical codes of honour and contrasting their actual behaviour with their (honest) self-images are non-economic levers for promoting honest behaviour,” Guszcza notes in a recent article he wrote in the January 2015 issue of Deloitte Review called The Last Mile Problem: How Data Science and Behavioural Science can Work Together.
Behavioural economics is not restricted to fraud detection. Guszcza observes that the applicability could extend to return-to-work or pre-accident level of functioning for injured insurance claimants. In fact, it is here that the confluence between data analytics and behavioural science is most evident.
“We can run predictive models and all sorts of analytics about the rates of return to work or normal living for certain types of claimants,” Guszcza observes. “But it is when you combine this with behavioural insights that it really gets interesting. If you are able to inform an individual that 90 percent of the people with the same injury go back to work within three weeks, I suspect it will give that person something to work towards. It might provide an unconscious motivator. It is a pro-social outcome,” he adds.
The use of telematics in tracking driving statistics is another potential example of the use of behavioural insights for economic and social purposes. Here, the interest is not just offering premium discounts, but improving driver safety.
“There is certainly a behavioural impact when it comes to telematics,” says Walter. “As that whole process is developing, I see more interest in the discussion around the behavioural side of interacting with customers-from a claims prevention approach rather than claims management standpoint.”
Telematics is not only valuable for underwriting but also “for pro-social nudges,” Guszcza adds. “You can give people a report card on their driving, how they compare with a survey of their peers. It gives insurance companies a way of increasing positive touch points and giving people a data service to help them understand their risk better.”
Other key elements of behavioural economics are the inter-linked concepts of “choice architecture” and “design thinking.” When it comes to choices in complex areas, such as insurance coverages, this means that options are simplified based on how people actually think and respond.
“Rather than design products or programs based on perfect rationality, we should go with the grain of human psychology,” Guszcza comments. ”So if an insurance company is going to offer a choice of coverage options, rather than this complicated menu of 40 possibilities, why don’t we use what we know about people-centric design and give fewer but more intuitive choices.”
Guszcza adds that the same approach can be used in various aspects of the insurance lifecycle-such as using social media behavioural profiles based on “digital breadcrumbs” for contact/channel preferences, claims triage, self-service options and so on.
“A lot of this stuff is just coming online. It is early days, but there are all these innovative and tangible ways in which insurance companies can be more customer centric,” Guszcza notes. “That should be the motivation-if you can provide unique services in a way that people find agreeable, you are offering a different kind of value that goes beyond just products and commodities. “With the power of suggestion and the appeal to social norms, some say that behavioural nudging could cross a line sometimes called the “creep factor.” Is it a positive force for ethical decisions or an intrusive form of social engineering?
“I think if you do all of this stuff from a product centric perspective, then it could be creepy,” Guszcza says. “If you are trying to think in terms of empathy, customer needs, what would help them in their daily lives, then it is beneficial. It actually gives people prioritized choices. You can call that paternalistic; I frame it as choice architecture and design thinking.”
Finding the exact point where data analytics meets behavioural science could be a challenge for an insurance industry that is not accustomed to being on the leading edge of innovation. This challenge is what Guszcza refers to as the “last mile problem.” Nevertheless, there are parallels in other industries-ranging from energy conservation to financial management-that show the power of behavioural insights and the proper use of data can be a potent combination.
“You can use understanding of human psychology to create little interventions that have disproportionate effects,” Guszcza concludes. “It is an almost cost-free experiment that can result in millions of dollars in savings and the achievement of a positive social outcome.”
Craig Harris is a freelance journalist who specializes in Insurance (P&C + life), Financial Services, including investing and wealth management.
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