May 10, 2013
Tackling Underwriting Profitability Head On
by Dax Craig
Expecting underwriters to take on today's challenges using yesterday's tools and yesterday's approach to pricing is no longer sustainable.
For many years, insurance companies built their reserves by focusing on investment strategies. The recent financial crisis changed that: insurers became incentivized to shift their focus as yields became more unpredictable than ever. As insurance carriers looked to the future, they know that running a profitable underwriting operation is critical to their long term stability.
Profitable underwriting is easier said than done. Insurers already have highly competent teams of underwriters, so the big question becomes, “How do I make my underwriting operation as efficient and profitable as possible without creating massive disruptions with my current processes?”
There are three core challenges that are standing in the way:
- Lack of Visibility: First, the approach most companies take to data makes it hard to see what's really going on in the market and within your own portfolio. Although you may be familiar with a specific segment of the market, do you really know how well your portfolio is performing against the industry, or how volume and profit tradeoffs are impacting your overall performance? Without the combination of the right data, risk models, and tools, you can’t monitor your portfolio or the market at large, and can't see pockets of pricing inadequacy and redundancy.
- Current Pricing Approach: You know the agents that underwriters engage with every day want you to give them the right price for the right risk, and it's not easy. In fact, it's nearly impossible. Underwriters are often asked to make decisions based on limited industry data and a limited set of risk characteristics that may or may not be properly weighted. As an underwriter reviews submission after submission, you need to make decisions such as, “How much weight do I assign to each of these risk characteristics (severity, frequency, historical loss ratio, governing class, premium size, etc.)?” Imagine how hard it is to do the mental math on each policy and fully understand how the importance of the class code relates to the importance of the historical loss ratio or any other of the most important variables.
- Inertia: When executives talk about how to solve these challenges around visibility and pricing, most admit they're concerned about how to overcome corporate inertia and institutional bias. The last thing you want to do is lead a large change initiative and end up alienating your agents, your analysts, and your underwriters. What if you could discover pockets of pricing inadequacy and redundancy currently unknown to you? What if you could free your underwriters to do what they do best? And what if you could start in the way that makes the most sense for your organization?
There's a strong and growing desire to take advantage of new sources of information and modern tools to help underwriters make risk selection and pricing decisions. The implementation of predictive analytics, in particular, is becoming a necessity for carriers to succeed in today's marketplace. According to a recent study by analyst firm Strategy Meets Action, over one-third of insurers are currently investing in predictive analytics and models to mitigate against the problems in the market and equip their underwriters with the necessary predictive tools to ensure accuracy and consistency in pricing and risk selection. Dowling & Partners recently published an in-depth study on predictive analytics and said, “Use of predictive modeling is still in many cases a competitive advantage for insurers that use it, but it is beginning to be a disadvantage for those that don't.” Predictive analytics uses statistical and analytical techniques to develop models that enable accurate predictions about future event outcomes. With the use of predictive analytics, underwriters gain visibility into their portfolio and a deeper understanding of their portfolio's risk quality. Plus, underwriters will get valuable context so they understand what is driving an individual predictive score.
Another crucial capability of predictive modeling is the mining of an abundance of data to identify trends, patterns and relationships. By allowing this technology to synthesize massive amounts of data into actionable information, underwriters can focus on what they do best: they can look at the management or safety program of an insured, anything they think is valuable. This is the artisan piece of underwriting. This is that critical human element that computers will never replace. As soon as executives see how seamless it can be for predictive analytics to be integrated into the underwriting process, the issue of overcoming corporate inertia is oftentimes solved.
Just as insurance leaders are exploring new methods to ensure profitability, underwriters are eager to adopt the analytical advancements that will solve the tough problems carriers are facing today. Expecting underwriters to take on today's challenges using yesterday's tools and yesterday's approach to pricing is no longer sustainable. Predictive analytics offers a better and faster method for underwriters to control their portfolio's performance, effectively managing risk and producing better results for an entire organization.