The insurance industry’s past success has been due, in part, to its cautious and risk-averse nature. But to succeed in the future, insurance companies have to adopt new technologies like predictive analytics, according to a blog post by Joe McKendrick in Insurance Networking News.
In the post, McKendrick points to a paper written by Deloitte’s Howard Mills that makes a strong business case for advanced or predictive analytics within insurance organizations.
Mills’ opinion is that predictive analytics will help insurance companies better understand future threats and opportunities. And Mills believes that insurance companies will be more successful by embedding analytics into their business processes, McKendrick explains.
Mills says there are five business areas where predictive analytics can help insurance firms operate more efficiently, according to McKendrick.
- Underwriting and pricing: “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,” notes Mills in his paper. Including analytics in an automated underwriting platform can help improve the quality of the underwriting as well as speed up policy writing and reduce operating expenses.
- Talent management: Psychometric data, which is used to measure an employee’s strengths and weaknesses, can 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,” Mills notes in his paper.
- Medical malpractice prediction: Predictive analytics can help insurance companies better determine whether doctors “are more likely to be sued for malpractice based on practice parameters and patient safety,” according to Mills.
- Consumer business: Analytics can help insurance carriers understand their customers as well as sales patterns better, Mills notes. Although it’s extremely effective, 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,” says Mills.
- 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,” Mill says in his paper. “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.”
McKendrick says advanced analytics can help insurance companies answer the following questions:
- 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?
But advanced or predictive analytics alone won’t help an insurance company be a winning company, says McKendrick.
“Analytics is not a substitute for good management, but one of its most effective tools,” Mills notes in his report. “Embedding analytics capabilities and outputs into processes throughout an insurer helps drive a culture of discipline and accountability.”
