Filed Under:Agent Broker, Agency Management

5 emerging underwriting strategies

An old industry learns new tricks

Most insurers now agree that tech advancement is no longer linear. It's exponential. (Photo: Shutterstock)
Most insurers now agree that tech advancement is no longer linear. It's exponential. (Photo: Shutterstock)

Insurance has a reputation for being slow to innovate.

But there's no denying we live in an era of disruption.

Related: How insurers can stay relevant in an era of disruption

According to Accenture, 87 percent of insurers agree that tech advancement is no longer linear. It's exponential.

Amid this culture of innovation, one of the areas to see the most change — and the most investment — is underwriting. Insurers and software developers are collaborating, thinking out of the box, and responding to a changing landscape of customer needs and expectations.

These industry innovators are also ushering in new models and trends.

Related: 5 fuels for your insurance innovation engine

Here are five emerging underwriting strategies to consider.

Macroinsurance would provide consumers with 'coverage across all sectors' in one consolidated policy. (Photo: iStock)

Macroinsurance would provide consumers with 'coverage across all sectors' in one consolidated policy. (Photo: iStock)

No. 5: Macroinsurance


"Imagine a unified insurance policy for a life, motor, home, liability, and disability, all tied under a single underwriting process," said Gabriel Olano at Insurance Business Magazine. He's talking about macroinsurance: the crazy notion that customers could get "coverage across all insurance sectors" in one consolidated policy.

Related: 5 artificial intelligence tools defining the future of P&C insurance

In a few years, that notion may not seem so crazy after all. Macroinsurance is in its infancy now, but with systems such as cloud computing already in hand, it's not out of reach. In fact, an insurtech firm called Sherpa is busy blazing the trail with new data sources, artificial intelligence, and deep analytics.

Related: Could macro-insurance become a reality? The one-policy-covers-all-risk approach

In an article for Insurance Thought Leadership, Mark Beading says the potential of macroinsurance could be realized if actuaries team up, if traditional data elements are blended with AI and advanced analytics, and if underwriters coordinate their human expertise across various fields with new data elements. "Any way you look at it, underwriting becomes a completely new game in the macro-insurance world," he said.

Microinsurance works much like conventional insurance, except that the premium and insured amount are a good deal smaller and policies use simple language that's easy to understand. (Photo: iStock)

Microinsurance works much like conventional insurance, except that the premium and insured amount are a good deal smaller and policies use simple language that's easy to understand. (Photo: iStock)

No. 4: Microinsurance

While macroinsurance weaves many coverage types into a unified system tailored to the individual customer, microinsurance breaks coverage into bite-sized chunks that those with low incomes can more easily afford.

"For Australians living week to week, protecting the possessions they have is vitally important, but the cost of that protection can be out of reach," said Justine Davies at CANSTAR. According to NAIC, that's why microinsurance exists: it's "an important tool for protecting the health and livelihoods of under-served low-income populations in emerging markets and developing countries.”

Related: Measuring microinsurance success

Insurers typically offset potentially low microinsurance profit margins by using innovative distribution channels. For example, if you start an insurance company in an emerging market, you will start digital because everyone has a smartphone which can send basic underwriting data and premium payments. 

Bima is a great example. In 2010, Bima noticed that mobile penetration was growing in Ghana so they set out to provide life insurance, personal accident and hospital insurance via a paperless smartphone application. Ninety percent of their policyholders, members of the lower income mass market, are first-time insurance buyers. Products are simple, payouts are small, and the impact is big.

Related: 5 high-tech challenges (and solutions) for today's independent agents

Likewise, Leapfrog invests in companies to bring financial services to emerging markets through a mission they call "Profit with a Purpose." The firm’s portfolio companies provide low income people with financial and health services that are hard to access in their home countries.

While the model is still being fine-tuned, the argument for UBI is hard to beat. (Photo: iStock)

While the model is still being fine-tuned, the argument for UBI is hard to beat. (Photo: iStock)

No. 3: Usage-based insurance


It's been some years since UBI and telematics made their splash on the industry. When they promised disruption potential, they weren't wrong. The idea of paying for insurance only when you need it — for the actual miles or hours spent driving or flying —has proven to be "one of the most interesting developments in insurtech," says Financial Brand author and Burnmark CEO, Devie Mohan.

Related: UBI: A tool for growth and inclusion

While the model is still being fine-tuned, the argument for UBI is hard to beat. It gives carriers a more reliably accurate way of scoring risk, steering away from indirect criteria like credit scores, gender, age and marital status – and away from the discriminatory decisions that can accompany the assumptions behind factors such as those – to a more objective method with more precise pricing.

Wikipedia defines data science as a multidisciplinary way to analyze and understand real phenomena by unifying math, statistics, information science, machine learning, cluster analysis, data mining, and a string of other methods. (Photo: iStock)

Wikipedia defines data science as a multidisciplinary way to analyze and understand real phenomena by unifying math, statistics, information science, machine learning, cluster analysis, data mining, and a string of other methods. (Photo: iStock)

No. 2: Data science


Insurtech is powered by data. But data science deserves a spot on this list, in and of itself.

Industry watchers agree: Insurtech startups now transforming insurance are proving the value of applying data science to insurance, delivering better risk analysis and predictive modeling, especially in commercial underwriting.

Related: 3 ways data science is changing commercial underwriting

While improved profit margins and enhanced customer satisfaction are two obvious gains, the benefits can be further broken down into less paperwork, smarter risk analysis, and more processing efficiency.

Among the insurance benefits of artificial intelligence to the insurance industry: It can help fight fraud by identifying patterns that humans may not detect. In fact, its algorithms have attained a 75 percent accuracy rate. (Photo: iStock)

Among the insurance benefits of artificial intelligence to the insurance industry: It can help fight fraud by identifying patterns that humans may not detect. In fact, its algorithms have attained a 75 percent accuracy rate. (Photo: iStock)

No. 1: AI-based underwriting


"With the advent of more machine learning algorithms, underwriters are bringing in more information to better gauge risk and offer more tailor-made premium pricing," said Adam Uzialko from Business News Daily. "On the back end, the insurance process is being streamlined to connect applicants with carriers more efficiently and with fewer errors."

For example, natural language understanding (NLU) lets insurers look at textual data sources like Yelp reviews and social media updates, making unprecedented use of a category of information that had been unavailable.

Another example? Advice. With AI, insurers can make coverage recommendations based on what customers divulge about their business and what insurers know about similar endeavors.

Case in point: Analyze Re provides real-time analytics technology which is tuned to the needs of the insurance and reinsurance industries. Part of its offering is a machine learning portfolio optimization toolkit which churns through large volumes of data to help underwriters to gain more insight from their portfolios and thus plan for more effective renewals. In practice what this means is that underwriters can test out thousands of different renewal scenarios such as rate declines and then understand how to approach at the time of underwriting. It’s not a case of the machine telling them what to do but rather giving them a way to apply the softer, more personal side of underwriting in addition to the harder risk metrics to find a balance that would be difficult if not impossible to achieve manually.

New tricks


They say you can’t teach an old dog new tricks. As this list proves, that’s certainly not the case in the insurance industry. We are already finding new ways to tackle old problems, and this list of five emerging strategies is just the beginning. For those who lean in to innovation, the future of insurance is bright.

William Fawcett is chairman of Haverford Bermuda Limited. He can be contacted through LinkedIn: Learn more at www.linkedin.com/in/wmfawcett

See also:

Underwriting transformation in the digital era

How carriers can leverage the power of big data

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