Insurance Analytics: Going Beyond Risk
The global insurance industry faces unique challenges with ever-increasing regulatory pressures, new product innovation, controlling operating costs and sustaining growth. Intelligent decision-making becomes imperative to facing challenging situations. The only substantial factor that can best support decision-making is data. Data analysis (analytics) provides organizations with a framework for decision-making, solving complex business problems, improving performance, encouraging innovation, and anticipating and planning for change while mitigating and balancing risk. In order to sustain and grow against the intensifying competition, the best choice for companies is analytics.
Most insurers are using very sophisticated analytical models in areas such as underwriting, where actuaries have been using risk models for years to underwrite and price policies. Another example of advanced analytics is in catastrophe modeling. By coupling internal data (e.g. historical loss trends) with external data (e.g. hurricane tracking) for placement into a catastrophe-avoidance model, insurance companies can avoid risk in high-tech ways.
Since the insurance industry banks their business models on the number of future claims and related risk exposure, it is vital for companies to streamline these areas and use intelligence wherever possible.
When it comes to some of the core aspects of the business, the evolution of decision-making has come a long way in the insurance industry. This evolution involves a lot of change within the organization in terms technology, people, processes and culture, and it's paying off. Huge volumes of data related to demographics, psychographics, claims trends and product-related information are starting to enable better risk assessment and management, new product strategies and more efficient claims handling.
But the one area where the industry needs to dramatically improve its usage of analytics is at the front end of the business in order to attract and retain customers.
Customer lifecycle analytics is a foreign concept to many in the insurance industry. Other industries, such as consumer products, online retailers and financial services companies, are leading the way, using analytics to drive the acquisition and retention of highly desired (and profitable) customers. In the insurance industry, customer lifecycle analytics can be applied to both customers and agent channels. If insurers can bring the same analytical rigor to the front end of their businesses that is applied elsewhere, growth and profitability would dramatically improve.
The good news is that most insurers have an abundance of customer and marketing data across their organizations, but they also have not leveraged its full potential. One of the prime areas where insurers can benefit from more robust predictive models is in customer behavior. Analytics can increase the lifetime value of the customer manifold, streamline new customer acquisitions and predict attrition of existing customers. Behavior scores aid in proper product mapping to target customers in order to optimize various marketing and product enhancement efforts. When it comes to deriving insights about customers, data (and how it is collected and organized) is vital.
Customer behavior scoring models that use the power of statistics to leverage internal accounts receivables, business demographics, invoice specific details, collection performance and other internally collected data have been found to be effective in identifying and managing risk. Models built exclusively for existing customers that are primarily based on actual performance are much more powerful predictors of existing customers' future performance than models based merely on external bureau data only. Behavior models are empirically derived and validated, using advanced multivariate statistical techniques to determine what information is relevant, and how important it is to solve the client's business problem.
Many insurers still encounter the dilemma of how to optimally apply analytics in their companies to unlock customers' potential. Most only have a vague notion about the business areas or applications that could stand to benefit. Second, most don't know how to get started: whom to hire, how to organize the project or how to architect the environment. All these initial hiccups will be sorted once they are able to track customer behavior and properly segment those customers. Continuous monitoring across the entire customer lifecycle is necessary to translate data into business insights and informed actions.
Insurers need to be more informed on how to collect and organize data, and use it optimally to derive maximum benefit. Introducing a culture of analytics to an organization and applying it across the customer lifecycle can be a major differentiator for an insurance company. Companies that embrace this and put it into action will reap the rewards.
Scott Staples is co-founder and president, Americas, of MindTree, Warren, N.J.