Insurance analytics is a combination of software and data that helps
insurers better understand their customers in order to provide them with
the best possible insurance products at the best possible prices. Insurance
analytics is usually applied to the use of information technology (IT) tools
to improve claims processing, reduce costs and increase customer
satisfaction.
Insurance analytics can help insurers manage risk and drive profitability.
The benefits of analytics include:
Better understanding of customers' needs improves sales opportunities.
Insurers can use data from third-party vendors or their own applications to
identify which customers are most likely to buy additional products or
services. For example, a life insurance company may find that an older male
customer who lives alone has low coverage on his policy but renews
annually anyway because he enjoys the peace of mind that comes with
having coverage. This gives the insurer an opportunity to offer him special
rates for long-term policies or other products that might appeal to him
specifically.
Faster, more accurate underwriting decisions. Insurers rely on data to
make decisions about coverage and pricing, but these decisions are often
based on past experience or guesswork. Analytics allow providers to
uncover new information that helps them make better decisions, faster.
Enhanced agent productivity. Insurers use analytics to automate tasks such
as claim handling and claims investigations, which frees up agents for more
valuable work. This results in higher customer satisfaction, improved agent
productivity and lower costs for the insurer.
Improved understanding of policyholders’ needs. Insurers use analytics
tools to identify patterns in how customers use their policies — for
instance, by age group — so they can improve their products and services
accordingly.
Identify areas of risk and prevent them from arising in the first place. This
is important for insurers that are trying to reduce their exposure to certain
risks, such as natural disasters or cybercrime. It can also help smaller
insurers with limited resources identify areas where they can make a
difference. For example, it was found that self-employed individuals who
bought health insurance coverage through online brokers or direct
marketers were less likely to incur medical expenses than those who
purchased coverage through employers or agents. In addition, it was found
that drivers with clean driving records were less likely to be involved in an
accident than drivers who had been involved in multiple accidents over the
past six years.
Insurance analytics can also help insurers reduce fraud. By analyzing data
on driver behaviour and claims patterns, insurers can identify potential
fraudsters and prevent them from getting benefits they are not entitled to
under the policy they purchased.
Identify new opportunities for growth, including new products, customers
or regions that have not previously been considered. For example, by
analyzing data on claims filed over time, an insurer may find out that claims
are being filed at higher rates in certain parts of its portfolio compared to
others. This could signal an opportunity for growth in those areas through
advertising or product development.
