Predictive Analytics Impact on the Insurance Industry
Predictive analytics is playing an ever-increasing role in virtually all areas of the insurance industry. Throughout the value chain of marketing, sales, underwriting, pricing and claims, predictive analytics are assisting more and more companies in better risk assessment, maximizing the return on their investments, improving customer service and increasing overall efficiencies. Its impact in the insurance industry is seismic, although not necessarily new.
A case could be made that predictive analytics got its start in insurance when Edmond Halley, of Halley’s Comet fame, published the “Life Table and its Uses” in the 17th century. Actuarial tables are still used, of course, but are a just a small part of predictive analytics now and in the future.
Overcoming Challenges: Predictive Analytics in Insurance
Many early concerns about predictive analytics, mainly those associated with the Fair Credit Reporting Act and involving consumer privacy issues, have been overcome by the consumer’s own willingness to share such personal information to receive greater value in the form of discounts or credits for better risk profiles. A paper on predictive analytics, published by Deloitte Consulting LLP in 2010, recognized these issues even back then and cautioned that predictive analytics must consider “regulatory, ethical, and cultural concerns”.
Another challenge for the insurance sector is its conservative nature. Analytics are broadly used but are primarily descriptive or historical. Most insurers want high confidence in their predictive analytics. Today’s models supported by new predictive tools and database technologies combined with Big Data offer far superior, secure and solid behavioral information than has been previously available.
Trends and Benefits of Predictive Analytics in the Insurance Sector
So, what are the “brass tacks” benefits of predictive analytics for the insurance industry?
Predictive analytics is widely accepted in marketing for customer acquisition, retention and cross-sell for good reason. Customers with more than one policy typically have a higher retention rate. Also, a policy often does not become profitable for 2-3 years due to sales acquisition costs. Predictive analytics help target the right customers and to predict those who may leave or churn.
In underwriting, predictive analytics enable better risk assessment and classification which leads to better pricing. Pricing analytics can be the “tipping point’ for consumers in choosing insurers and products. They also lead to better profitability for insurers as they target desirable customer segments and support real time, dynamic pricing.
Predictive analytics is also an important defense against insurance claims fraud. According to the Coalition Against Insurance Fraud, insurance fraud is one of America’s largest crimes, with at least $80 billion in fraudulent losses each year. These losses don’t just affect insurers, but policyholders as well since ultimately premiums must reflect all losses. Predictive analytics can better detect and “flag” potential fraudulent and duplicate claims.
From a finance perspective, by combining past losses with predictive analytics on future losses, the ability to manage loss reserves is also greatly enhanced. Funds can be more efficiently allocated, improving margins through improved cash management.
SAP’s Customer Retention and Leonardo
One of the greatest challenges in predictive analytics is scarce data science resources. SAP can help insurers jump start or refine their predictive analytics and increase the effectiveness of their existing analytics teams with SAP Leonardo.
SAP offers a Big Data solution to help predict customer churn based on behavior in reaction to events and other indicators. Often insurers’ current approaches have data limitations, aren’t customer-centric, and offer little insight into customer churn. SAP Customer Retention offers features like the ability to mine data, predict customers at risk of churning, create customer “fingerprints”, and better visualize the customer journey. It can monitor early indications of churn and take steps to prevent it. It also integrates with other SAP Products for full digital transformation.
SAP’s Leonardo opens new opportunities in the insurance sector as well. Big Data can be leveraged in the insurance business to take full advantage of the digital economy, including the use of powerful and valuable predictive analytics. Like Halley’s game-changing “Life Tables” of the 17th century, SAP is here today to help you take the next step into the promising analytics future of the insurance industry.
For more information on predictive analytics, please visit SAP Leonardo: Enabling organizations to store, manage, analyze data, and gain valuable insights.