In current economic climate where budget reductions are common, executives are under pressure
to deliver profitable growth. Business leaders must identify and implement the critical items that
will enable the enterprise to remain competitive. Methods and techniques long utilized in the
actuarial department are being leveraged to improve many areas of the insurance operation. Will
these methods really deliver value, or are they just empty rhetoric? Today’s soft market and other
economic challenges make it more critical than ever that insurance company leaders maximize
every dollar invested. Carriers, facing myriad challenges, are seeking opportunities to improve
operating efficiencies and gain a better understanding of distribution channels, customer
behavior, product and pricing efficiencies, risk selection and earlier recognition of problems before
the impact is felt.
How Predictive Analytics benefit
Despite the challenges, the current environment also creates the opportunity for insurers to outrun
their competitors by improving enterprise agility and optimizing operating costs. The winners at
the end of this economic cycle will be those who have taken the time to make the necessary
improvements to quickly capitalize on opportunities provided when the cycle turns up.
Having the ability to accurately forecast performance in activities as diverse as operations, budgets,
supplies, product demand and performance are crucial for business success. However, developing
a better understanding of existing processes, customer behavior, identifying opportunities that
may have been unexpected and anticipating problems before they happen, are also critical to the
2 success of any company. Predictive analytics can provide critical insight into these and other areas.
It is effortless to see why using predictive analytics well is imperative to insurance organizations,
which are particularly reliant on predicting future activities. An insurer’s ability to forecast a policy’s
ultimate cost determines how accurately it prices its product and, in turn, the extent to which it can
avoid adverse selection. In the fight for market share going on today, accurate pricing based on
2 policy performance is one of the critical areas.
It might be said that insurance carriers have always relied on forecasting, and that would be a
correct statement. Initially, insurers simply guessed at appropriate premiums. Subsequently, in
most cases led by the actuarial team, they determined premiums by analyzing a single factor, such
as the age of an insured building or the piloting history of an insured ship’s captain, both examples
of univariate analysis. As insurance operations became more technologically advanced, multiple
factors such as the age of the insured building, its type of construction, its usage, and so forth were
used to determine an appropriate premium, multivariate analysis.
Insurers use techniques known as predictive analytics to determine many critical items. Internal
and additional external data - information such as credit scores or local economic conditions that
may be relevant or correlated with a potential insurance outcome are used to deliver key elements
of an insurance solution. The use of predictive analytics has quickly become an insurance industry
best practice. Insurers use predictive analytic techniques to target potential clients, identify the
way they approach an insurance purchase, determine more relevant products for a specific market
as well as more accurate product pricing and to proactively identify potentially fraudulent claims.
This whitepaper will provide an overview of predictive analytics and present some drivers of its
growth, its uses in the property and casualty insurance industry and the advantages for insurers
who use it.
In this white paper, we provide an overview of predictive analytics and present some drivers of its growth, its uses in the property and casualty insurance industry and the advantages for those who use it.