When good insurance customers pay for bad ones
The goal for the insurance industry should be to avoid adverse selection at all costs.
The recent reactionary response to combat the profitability problem for many P&C insurers has been drastic to say the least. Insurers have pulled out of markets, stopped writing policies or chose to “quiet quit” profit-challenged states.
Personal lines customers experienced rate increases to the point many are now choosing to significantly reduce coverage or not carry insurance at all due to affordability issues. In fact, in just one year, auto insurance rates have jumped by 26% across the U.S., according to Bankrate, with even the “good” drivers paying an average of $2,543 annually in 2024.
It’s no wonder, then, that we have seen an uptick in personal lines shopping. Policyholders who stayed with the same carrier for years are becoming increasingly reactionary themselves as they look for lower rates.
TransUnion reports that over the past 2 years, the insurance shopping population has had the highest number of high-credit score consumers than previous years. Insurers are losing their most loyal, most profitable customers because they are bearing the brunt of the profitability problem. The best customers are seeing rate increases, even in cases where they have no accidents and a clean driving history. This puts every insurance carrier at risk, as losing their best customers will further add to their profit challenges.
The goal for the insurance industry should be to avoid adverse selection at all costs.
Know your policyholder — completely
One of the first solutions to help profitability is to know your customers, and know them completely. Insurers can do this by taking advantage of data innovation.
In fact, McKinsey reported that insurers using advanced analytics for customer insights and engagement see between a 5% and 20% increase in customer satisfaction and lift to revenues of 10% to 15%. It’s the best-in-class performers who are gaining a competitive advantage by elevating what it means to “know” customers, personalizing the customer journey, and identifying the best-fit customers even before sending a quote.
For example, by using advanced data and analytics in areas like marketing and underwriting, insurers can provide a seamless, expedited experience to the low-risk customers while triggering in-depth underwriting for higher risk prospects. The key to this strategy is to know your customer at the earliest possible point, before they even complete an application.
Advanced data can drive more informed decision-making in the risk selection process by identifying the best-fit customers. That ultimately protects the insurer and provides a better experience for those low-risk consumers.
Additionally, data innovation allows insurers to optimize marketing efforts. Instead of cutting advertising spending, carriers can focus on retention strategies and attracting the lowest risks. The imperative is to avoid adverse selection and understand which customers have an adequate rate — as well as those who fall outside the rating plan — at the earliest possible point.
Underwriting processes and rules improve because more low-risk customers can pass straight through processing, and underwriters only have to review the most complex risks; a much smaller portion of the portfolio. This becomes a distinctive customer experience for the policyholder, and one in which the insurer also gets to reap the benefits.
Beyond basic risk segmentation
Another area where advanced data and analytics can help is in risk segmentation and business rules. The majority of personal lines insurers employ only basic risk stratification models and underwriting criteria that are based on legacy practices. Few are implementing advanced data-driven techniques. But those that are holding out are really missing out.
Adopting a more proactive approach to evaluating risk occurs through incorporating data intelligence to identify risk characteristics earlier, which can help improve pricing and reduce losses. Insurers also can bind personal lines policies at rates more accurately and fairly, while again, enabling larger numbers of customers to be sent to straight-through processing.
Rate increases are not the only option.
Insurers must move beyond using only one strategy — rate increases — in their return to profitability. The hard market calls for a multi-pronged approach to stay competitive and keep your best customers while improving your combined ratio. Advanced data and analytics are enabling insurers to tap into external data sources in such volumes and at incredible speeds, profitability problems become resolved through enhanced risk assessment methods. Insurers are awarded the benefit of instead of merely using a few select attributes to assess the risk of a consumer, they expand the variables that are applied at every individual risk assessment. This is the kind of ‘personalization in knowing your customer’ that provides the great benefit of boosting pre-underwriting and prospect loss modeling capabilities at a foundational level.
The road ahead
Profitability problems, high combined ratios and loss ratios, inflation, and market uncertainty can all be overcome with advanced data and analytic solutions. In insurance, we’re from an industry that is no stranger to innovation. Insurance pioneered the shaping of technology and data’s direct impact on business and economy for decades. Insurance has always played an integral part in the world’s economic health, and today, it is even more critical that we remember our story.
The insurance industry is trying to address its current challenges while penalizing the key stakeholder we strive to protect — the customer. While higher repair costs, inflation and other macroeconomic factors are contributing to the rising loss costs associated with writing in personal lines, the weight of the profitability problem should not be passed onto the best customers when simple innovation can create better solutions that are a “win” for everyone.
Shannon Shallcross (shannon.shallcross@pinpoint.ai) is head of Client Services for Pinpoint Predictive. Shannon is a Tedx speaker who has coached dozens of data and Insurtech startups. She previously spent 12 years with Amica Insurance.
These opinions are the author’s own.
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