Those that forget history may be doomed to repeat it, but those who don't understand the future may never get there. The insurance industry may have succeeded in the past by being cautious and risk-averse, but it's time to start getting more attuned to a fast-changing world, one expert has been warning.

In a recent post, Barbara Geraghty of Business 2 Community points to an interesting paper written about a year ago by Deloitte's Howard Mills, which makes a strong business case for advanced or predictive analytics within insurance organizations.

“Given that typically the insurance industry has not only profited from conservative behavior in the past, but survived the financial crisis of recent times to a great extent because of it, the advantages or necessity of making such a change may not seem necessary to all,” Mills writes. “But nothing stays the same. C-suite occupants who think they still have time to ponder instead of acting to embrace advanced analytics may do well to ask: How quickly did the CD vanquish vinyl? How long before the iPod became king of the hill, relegating the CD to an afterthought? What happens if you miss the inflection point? Who’ll get run over if you fall behind the curve?”

Predictive analytics will help insurance leaders better understand what threats and opportunities are around the corner, and some further down the road as well. Mills advocates that insurance companies strive to better embed analytics into their business processes. As he defines it: “Predictive modeling is a refinement of the very human process of inductive reasoning. It is not necessary for business leaders to understand the finer points of multivariate regression, classification and regression trees, artificial neural networks or support vector machines. It is important for them to understand that these are all tools that can “learn” from large databases of cases to arrive at general conclusions.”

Mills points to five business areas in which analytics can improve insurance organization operations:

Underwriting and pricing: Multivariate scoring models, designed to better select and price insurance risks, “find gaps in traditional risk assessment and underwriting methodologies, and thereby, provide novel ways for insurers to better distinguish between seemingly similar or identical risks.” Working analytics into an automated underwriting platform can help improve the underwriting quality and create low and/or no-touch processing to clarify appetite, speed up policy writing and reduce operational costs.

Talent management: Psychometric data helps to predict employee performance. “In one study, we found that employees with certain combinations of behavioral traits had twice the chance of being promoted, whereas employees lacking a different combination of traits had virtually no chance of being promoted.”

Medical malpractice prediction: Predictive models can more effectively determine whether physicians “are more likely to be sued for malpractice based on practice parameters and patient safety.”

Consumer business: Analytics helps carriers better understand their customers and sales patterns. While extremely effective, Mills observes, many companies use their data “only to generate business metrics and fairly standard management reports. The data exist but are not being used to refine decisions rooted in intuition and mental heuristics.”

Claims and medical case management: Medical case management models “combine medical (diagnoses and co-morbidities), biographic, demographic and psychographic information to more-effectively predict which cases are more likely to exceed industry standard norms for severity and duration. With improved case management tools like these, workers can be helped to return to work more efficiently and abusers of the system can be more easily identified.”

Here are just some of the questions advanced analytics can help answer:

What is your company doing to understand the needs of its customers at their various life stages?

How much do you know about their appetites for risk? How much education do they need on risk management?

How do you find the potential customers who “get it," or educate and change into potential customers those who don’t?

What gaps are there in your product line?

How do you begin to find new customers and create products that appeal to them?

What price point?

How do you know which producers are really doing a great job?

How do you move those doing a good job to great?

How do you hedge your risks using reinsurance?

Of course, analytics by itself will not produce a winning company. Mills cautions that “analytics is not a substitute for good management, but one of its most effective tools. Embedding analytics capabilities and outputs into processes throughout an insurer helps drive a culture of discipline and accountability.”

Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.

Readers are encouraged to respond to Joe using the “Add Your Comments” box below. He can also be reached at

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