Consumers are becoming increasingly demanding about the way they purchase products and services – a shift that is affecting every industry from consumer product goods to health care.
This new competitive landscape – where consumers expect the seamless choice and accessibility that online retailers offer – is also challenging the insurance industry, according to a study from PricewaterhouseCoopers.
The research report, “Coming to Grips with Market Transformation,” summarizes key findings from interviews with 92 insurance CEOs in 39 countries.
The report notes that the insurance industry is poised to evolve from a “reactive claims payer to preventative risk manager,” a move that’s powered in large part by data analysis and data discovery.
“The next wave of big data and predictive modeling will allow insurers to move from insight to foresight, where they can tailor interactions and pricing at a customer level and use real-time data for decision making,” the report notes.
Nearly 60% of insurance leaders are concerned about the shift in consumer spending on insurance products and related behavior, a significantly higher proportion than in banking (50%) and asset management (44%). The report goes on to note that 86% of insurance leaders plan to increase their investments in technology over the next 12 months, more than any other commercial sector surveyed.
“To stay in the game, [insurance companies] will need to think and act at the same rate as technology and customer expectations evolve,” the report says. “They will need to know how competitors are making better use of new sources of data and analytical techniques in order to engage more closely with customers and price more keenly, as well as if new competitors are even going to come from inside the industry.”
One way to do this is to leverage data discovery tools that empower users to quickly combine disparate structured and unstructured data sources without custom scripting and IT support, according to the recently released Gartner BI Magic Quadrant for Business Intelligence and Analytics Platforms.
According to the PwC research, the most cutting-edge insurance companies:
- Use customer analytics to understand and segment their customers and distributors more effectively.
- Look beyond the industry to scan for emerging threats and opportunities.
- Use customer profiling and risk analytics to provide a new generation of fully customized “smart” policies like using telematic sensors to gauge how carefully people driv.
- Analyze the payments, social media and other digital trails that people leave to get more control over their exposure.
In addition to tapping data analysis to more effectively gain and retain customers, insurers also are embracing predictive analytics to help them predict and manage risk in their organizations, according to a study from Accenture.
The 2012 Risk Analytics Study finds that Property and Casualty (P&C) insurance companies are seeking to improve their abilities to respond quickly to – and predict – risks. On the other hand, life insurers and annuity providers are working to define new products, optimize their overall portfolios and improve the performance of their assets.
Additional findings of the report include:
- Sixty-five percent of life insurers affirm the value of risk analytics.
- Sixty-two percent of P&C insurers foresee increasing their investments in risk analytics by more than 10% over the next two years.
- The most important reasons for risk analytics are risk selection and pricing (63%), fraud (61%), and investment portfolio optimization (50%).
- In general, insurers are not highly confident in their modeling capabilities: only 55% rate themselves as above average or excellent.
- Only 54% of life insurers rate themselves either above average or excellent in their reporting and dashboard development capabilities, with 63% of P&C insurers rating themselves either above average or excellent.
To bolster their risk analytics, the Accenture report recommends that insurers:
- Integrate specific analytics capabilities across claims, underwriting and distribution.
- Embed risk analytics with management processes.
- Link risk models to obtain a fully aggregated view of all types of risk.
