By John Johansen

For decades, the insurance industry has collected data and filed it in cabinets, and never in a very sophisticated way. While this is not a new realization, the past four or five years have seen increased efforts by insurance companies of all shapes and sizes to get control of critical data and put it to good use. The growing awareness of insurance companies that data which was once static can be useful, and even actionable, is indicative of the growing sophistication of the insurance industry.

Advancements in technology and distribution are making more powerful tools for making the most of existing data available at lower costs to smaller companies across the insurance industry. This creates opportunities for all insurance companies to use and benefit from the power predictive analytics can provide over strategic business operations, successes and failures.

Predictive analytics (PA) in its simplest terms is the use of technology to produce an analysis of current and historical data combined for the purpose of making informed decisions about future events. PA looks for correlations in sets of data and establishes a relationship between data points. Essentially, PA identifies patterns that may not be easily viewed from the surface, thereby allowing insurance companies to take existing data and use it to proactively stay ahead of the risk.

Predictive analytics can have immediate benefits for any insurance company regardless of size, lines of business written, number of employees, direct written premium (DWP), depth of internal technology knowledge, or current state of the company's insurance data. Insurance companies looking to distinguish themselves past a price point should not wait for the moment when the company determines the data is perfect, now is the time to act.

Predictive analytics utilizes the increased complexity of modern modeling methods to capitalize on both the breadth and depth of multiple data sources. In addition, internal data mining is used by predictive analytics solutions to access a broad array of underwriting, loss and customer data in order to identify predictive factors or clusters of factors. In many cases, data warehousing or other business intelligence (BI) initiatives are a key factor in enabling predictive analytics. But keep in mind that predictive analytics is different than BI.

External data mining is often used to bring in data from third-party providers which is then matched with internal data to provide useful comparisons and contrasts to a company's own set of data. Unlike BI, predictive analytics utilizes modeling to find predictive elements through sophisticated multi-variant analysis, and then helps insurance companies to create scoring mechanisms within existing rating or claims algorithms to further automate decision-making.

But, no matter how many glowing reports or testimonials you read, the proof is in the pudding so to speak, and you need to know how implementing predictive analytics will impact your bottom line before allotting critical company funds that are tighter than ever in today's economy. Some of the biggest benefits of predictive analytics include:

Providing your company the ability to net new business;
Enabling you to get more business from existing customers;
Allowing your company to write business more profitably; and
Improving loss prevention through better claim management.

Additionally, incorporating these tools can go a long way toward automating decision-making. By building models into algorithms for rating and claims, incoming data can be analyzed and diverted into various workflows before ever reaching human hands. By providing this analysis upfront, predictive analytics helps isolate complex risks that need more attention from simpler risks which can run through an automated approval process. That means all departments, from marketing and underwriting to claims and auditing, can be influenced and improved through the use of these powerful tools.

Beyond dashboards and scorecards, management can get a more holistic view of an entire business by implementing predictive analytics. With accurate reporting, a fresh view of risks, and profitable prospects or customers, management can clearly picture where the company needs to focus in the future. It becomes possible to hone operations and manage the company strategically in order to get there.

Engaging industry experts with deep domain knowledge can be helpful to walk you through the process. With the right pieces in place, you can benchmark data against industry averages and begin to see insights right away. It is an emerging technology but by implementing predictive analytics, the payoffs are actionable.

Now is the time to get serious about bringing all of your data together and not only measure where you are, but where you need to go and the best ways to get there. Forward-thinking insurance companies know that predictive analytics toolsets are coming down market and becoming more accessible to small and mid-tier companies. Your competition is looking at predictive analytics, why aren't you?

John Johansen is a founding partner of Agile Technologies.

Special Reports /

Predictive Analytics Delivers Benefits for Insurers of All Sizes

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