Insurers are at risk of falling far behind other industries when it comes to making use of modern technologies like advanced analytics.

That’s a problem, because if there’s one thing insurers don’t like, it’s risk.

A PwC report outlining the industry’s top issues for 2015 leads with this potential exposure, calling out insurers for lagging behind other industries in bringing modern analytics into their organizations.  Yet the report shows what’s possible with the right analytics solution, including tackling hard-to-answer questions: “Can we optimize pricing by capturing new health data to apply to our underwriting process?” “How can we improve policy holder persistency?” “What is the most cost-effective path for managing the flow of policies?”

For insurance professionals, quickly and accurately answering questions like these can mean the difference between a good year and a bad one. However, with many insurance companies still using applications mounted on (and often written for) legacy mainframe systems, answering the tough questions takes significant time, if it is even possible at all.

In its report, PwC details what new technologies can be found in a modern analytics system – technologies that, the report says, “are radically different from the legacy technologies that most insurance companies use today.” They include:

1.     In-memory computation. Answering the kind of questions listed above – using queries that involve multiple parameters and numerous data types from several sources – typically requires hours of computational time on systems designed more for transaction processing than analysis. Modern solutions take a different approach. Tidemark Planning and Analytics for Insurance, for instance, uses Apache SPARK to process large data sets across clusters of computers. This in-memory computational architecture gives insurers the resources they need to make complex queries requiring unlimited dimensionality, which the rigid structures found in legacy platforms aren’t designed to support.

2.     Granular analysis. Though the PwC report doesn’t explicitly use these words, granular analysis is all over the examples it chooses to illustrate the power of modern analytics. That’s because you can’t model useful what-if scenarios about specific customer behaviors without drilling down into the meaningful details that will reveal what drives those decisions – details that are all but invisible in the summary-level views that legacy systems traditionally provide.

3.     Natural language processing. PwC hits the nail on the head here, because natural language processing uses easily recognizable semantics so more stakeholders can engage in useful analysis, once the sole domain of highly trained analysts and data scientists. This means regional managers can find out policy counts by line of business within their territories, while sales and marketing managers can understand where to implement campaigns to increase sales of certain products.

4.     Collaboration. An advanced analytics platform must be collaborative, notes PwC, because roles within insurance companies have become highly specialized. Tidemark Planning and Analytics for Insurance is designed as a fully collaborative platform that every stakeholder can access and interact with using virtually any device. Unlike legacy platforms, Tidemark delivers a self-service environment built around recognized insurance metrics, business processes, and workflows, with instant message conversations, emails, supporting documents and other collaborations always available wherever they are relevant in the application. This makes it easy for employees in all roles to shape a crucial forecasting model leveraging their fields of expertise.

While I agree that PwC’s report lays out key criteria, I’d like to add to it. Two recent advances that insurance companies need to consider when trying to reduce risk and capture market share include:

5.     Predictive forecasting. As insurers discover that historical trends are becoming less reliable indicators of future events, traditional methods for forecasting revenues, expenses, loss ratios and policy holder persistency are beginning to fail. What they need isn’t a generic forecast for straw man situations, but a picture of the most likely outcome from virtually any scenario.  This, however, requires massive computational capacity and sophisticated technologies like machine-learning algorithms. Tidemark provides both, so rather than trying to predict the renewal behavior of a term life policy holder simply by categorizing him as “male 40 non-smoker,” insurers can build accurate forecasts incorporating useful variables like household income, healthcare spending, local cost of living data, regional hiring statistics and more.

6.     Competitive benchmarking and analysis. In any given market, all competitors generally chase the same dollars. This means successful companies must outcompete other insurers – and increasingly, that means out analyzing them.  For instance, what if you could analyze any product, category or company, and then use those insights to create performance benchmarks that tell you exactly how you’re doing against your competitors? Tidemark built this very capability into its latest release with Tidemark Compete™. For the first time, you can pull in any proprietary or 3rd-party data source, unifying it with financial and operational data to gain market share analysis sliced by state, region and ZIP code. Then correlate the data to your company's performance to model what-if scenarios help you identify your best bets for growth.

If the future of the insurance industry lies in the ability of insurers to harness the power of advanced analytics, then I encourage you to request a personalized demo of Tidemark Planning and Analytics for Insurance. Some of the most recognizable brands in the industry have implemented Tidemark to help them shorten planning cycles, reveal revenue opportunities, and develop forecasts that have helped them more closely align premiums with risk.

Insurance companies are rewarded for avoiding risk. For many, this has meant continuing to rely on the planning and analytics platforms they’ve used for decades. But when it comes to the technologies that will help them compete in the future, sticking with the status quo may turn out to be the biggest risk of all.