No longer confined to technology, big data has become integral to providing solutions to the insurance industry's long standing challenges. At the heart of the industry, underwriters evaluate the risks of insuring a particular person and set a premium for the policy accordingly. The use of financial data, actuarial data, claims data and risk data cover virtually every important decision an insurance company makes.
While the industry has made progress capturing and analyzing the majority of structured data associated with their policyholders, the volume of untapped unstructured data remains just as valuable. Unstructured data refers to information sharing sources such as real time news feeds, social media and other mobile channels.
To create a competitive advantage and succeed in that dynamic environment, insurers must leverage the value of big data. As underwriting continues to drive insurance pricing, big data and analytics have also had profound effects on customer insights, claims management and risk management.
Insurance Industry Structure
At its core, the insurance industry encompasses the management of an individual’s risk. Between life, health and liability insurance, companies collect premiums on policies and invest them in holdings until a claims is requested. If the maximum amount paid out is greater than premiums collected, the initial policy underestimated the individual’s level of risk.
A number of factors are constantly being calculated to ensure appropriate policies are being issued. An actuary helps design insurance policies using past information to analyze the financial consequences and risks. Likewise, an underwriter will utilize actuary data along with financial data and claims reports to decide the appropriate level of coverage and the terms of coverage. If the price is too low then profit margins may be inadequate, and if prices are very high then customers won’t buy policies from the company.
As the insurance industry trends toward highly competitive, companies must differentiate themselves through low cost structures, greater efficiency and customer satisfaction. In a technology driven economy, big data inspires new ways to transform these processes while also meeting evolving regulatory compliance. (For more, see: The History Of Insurance In America.)
Following technology and communication trends in conjunction with the explosive growth of data, the economy has empowered "customer-centricity." Changing customer preferences has put pressure on insurance companies to create simpler and more transparent products. Predicting customer behavior and obtaining insight into value is critical to developing and optimizing claims that result in improved customer retention and profitability. Applying insights to customer call centers, customer retention analysis and customer behaviors, insurers can better route customers to the appropriate support.
Traditionally, policies were determined based on historical information. However, the customer experience is now dictated by direct and indirect channels. Direct interactions include call centers and insurance agents while indirect channels include social media and marketing campaigns. Through a dynamic environment, engaging customers and meeting customer expectations, insurers need to focus on redefining customer relationships and transparency.
A part of being insured is the ability to file a claim. An insurance claim is a formal request to the insurance company for payment following an event within the terms of the stated policy. Needless to say, fraudulent claims are a plague for the insurance industry. It is estimated that 1 in 10 and nearly $80 billion annually in fraudulent claims are made annually in the United States.
Predictive analytics can play a critical role in addressing rising fraudulent claims and losses. At the underwriting stage of a policy, insurance companies can rapidly analyze voluminous data in order to detecting fraud-likely applicants. During the claims request, companies can leverage internal data sources with unstructured data to identify whether the claim is legitimate. Real time monitoring, through social media and digital channels provide greater insight throughout the claims cycle.
Fraud detection not only benefits the insurance company, but as a result legitimate claims can be processed more efficiently. (For more, see: Is My Health Insurance Good Abroad?)
The changing nature of the insurance industry has brought on new risks from catastrophes and regulatory compliance. As a result, risk management becomes more important to the organization. In particular catastrophe risk modeling predicts the maximum potential loss from a catastrophic event. With big data and analytics, insurers can model policies integrating historical data, policy conditions, exposure data and reinsurance information. Likewise, underwriters can price catastrophe policies based on granular factors rather than by city and state. A big data driven solution allows pricing models to be updated in real time rather than a few times a year.
Traditionally, the nature of frequently changing reforms and adoption of regulations has proved to be costly to insurance companies. Insurance companies are routinely subject to examinations and any incompliance can result in public scrutiny, fines and a tarnished reputation. Many federal regulations including Basel III, Solvency II, Dodd-Frank, and RMORSA Model Act, require the insurance industry to jump through many, difficult bureaucratic hoops. To help meet compliance changes and cut costs, algorithms based off big data can meet increasing regulatory demands. By dynamically monitoring and adhering to compliance, organizations can improve decision making and minimize losses.
The Bottom Line
With major impacts already in finance, marketing, and healthcare, integrating big data and analytics in the insurance industry has been slower than expected. Despite its inherent benefits, significant challenges impede the adoption of big data by insurers.
Notably, there is a shortage of individuals possessing data analytics skills with expertise in the insurance industry. As a result, data from internal and external sources are unable to be effectively integrated into a single data set. Due the highly competitive nature of the insurance industry, companies who have successfully integrated big data and analytics have created a competitive edge by implementing low cost structures, greater efficiency and proactive customer engagement.