Originally published March 30, 2015

Big Data is bringing decisive advantages to insurers through more relevant, detailed, and up to date information than was previously possible. At the center of this transformation are customers. They expect to be able to select products defined and driven by their needs, preferences and convenience.

Insurance executives globally are trying to work out what all this means for their customers, their operating models, their bottom lines. Some are successful in finding ways of harnessing the power of digital to drive change and improve customer experience. Some are struggling. Some have other priorities.

Studies estimate that 50% of current jobs in Europe and North America will be replaced by automated robots and computers within the next 20 years. This disruptive change is hitting the insurance world particularly hard, as other studies have shown that 99% of the work of an insurance underwriter can be replaced by a computer. With this in mind, it is not surprizing that the Big Data frontline is largely populated by major insurance companies, as the cost of doing nothing is impossible to overcome.

I present below seven transformation cases which show the benefits Big Data analytics can give insurers and the challenges they might have with the transformation to a data-driven organization.

Automated underwriting

Through automated underwriting, life insurers can reduce the underwriting overhead and create affordable products to gain access to new markets. Data from multiple sources are combined to evaluate each applicant’s risk profile using statistical methods. Insurers already using automated underwriting can reduce the manual labour by 95%. In other words, the prophecies from the studies mentioned earlier are already becoming reality.

Product individualization

Big data creates increasing knowledge of the customer’s needs and behavioral patterns. This can be used to deliver insurance products specially tailored for the individual customer, at an individual premium. Insurance companies doing this have seen improved customer satisfaction and have been able to access previously inaccessible markets. Instead of finding the right customer for the product, insurers are developing the right product for the customer.

Customer retention

Life insurers that can develop a stronger relationship with the customer through quality of services and social media enjoy stronger

customer retention. This has already been observed in insurance products using wearables and telematics data to track customer activities and encourage healthy lifestyles to improve longevity and reduce risks.

Conduct risk & compliance

Conduct risk and mis-selling are directly linked to understanding the client needs gained through big data. By combining this knowledge with salesperson statistics the conduct risks can be accurately estimated. KPIs linked to customer satisfaction metrics can be used in addition to sales volume to encourage good sales behavior.

Sales channel optimization

Utilizing detailed information on how users interact with the company website, such as navigation history, time spent on each page etc, can help optimize the structure and layout for the customer, increasing sales.

Marketing campaign selection

Marketing campaigns that target the most valuable segments of the population that are most likely to buy the product give a better return on investment.

Price optimization

With an internet search a customer can today in minutes compare her current insurance with competing offers. Price sensitivity is increasing, and the insurer that has the most accurate risk assessment can make the best offer.

What specific challenges do we see globally?

• Assessment in an increasingly digital world
• Making best use of the vast amounts of available data
• Taming the challenge of legacy systems
• Keeping the business secure given increased cyber threats
• Attracting and retaining the people needed to drive the change