This very good Article by Anand S. Rao discusses the growing use of predictive analytics in the Insurance Industry. I believe Mr. Rao is right on the mark – although I continue to emphasize the expanding role of Text Analytics in the analytic value equation. In this article, he identifies some of the drivers of predictive analytics adoption.
Mr Rao’s thoughts on Predictive Analytics Adoption
- Accelerating technology and consumer adoption. Advances in sensor, computing, and communication technologies are adding to the volume of data and enabling the analysis, interpretation, and visualization of it. The rapid growth of mobility and mobile data use, coupled with social networking, has resulted in exponential growth of behavioral data. The “Internet of things,” which makes devices from automobiles and plant machinery to dishwashers and washing machines relay real-time data, substantially increases the amount of information generated. Smart sensors that cost just a few dollars can measure a single attribute, such as temperature or moisture, and communicate the readings on a real-time basis. As these technologies improve and their use accelerates, they will increase the amount of available information.
- Increased data availability. Consumer adoption of rapidly evolving technology and automation has resulted in the generation of billions of gigabytes of data by insurers, government organizations, regulators, non-profit organizations, rating agencies, and independent data aggregators. By some estimates, the production of data has increased from 150 billion gigabytes in 2005 to 1,200 billion gigabytes in 2010. As insurers have started integrating their legacy systems, externalizing data into enterprise data warehouses, and developing a single view of the customer, their internal company data sources have significantly improved in terms of quality and quantity. Moreover, external data sources have become more standardized, allowing for greater sharing of data. Non-profit data aggregators and for-profit data providers have facilitated the flow of information between different parties.
- Sophisticated analytical techniques. The need to generate faster and better insights from increasing multi-media data is resulting in fresh techniques for analyzing text, speech, video, and sentiments. Analysis of online interactions, unstructured text, speech, video, and social data mining have all emerged as distinct areas of focus.
Mr. Rao also does a good job of summarizing the key applications of predictive analytics across several functional areas, as described below:
- Claims management. According to the Insurance Information Institute, fraud—most commonly, staged accidents and claims padding—costs P&C insurers more than $30 billion annually. By analyzing historical claims information and demographic profiles, predictive models can identify potential fraudulent cases for further investigation. This allows claims adjusters to focus on suspicious cases and conduct more detailed investigations. Predictive analytics can also reduce losses. By analyzing the types of claims, predictive models can flag cases that might be subject to litigation. Routing such claims through specialist adjusters and streamlining the process can help adjusters reduce litigation costs. As a result, predictive analytics can contribute to reduced fraud costs, reduced loss adjustment expenses, improved adjuster productivity, and reduce the overall claims ratio.
- Demand management. Insurers use a multitude of distribution channels to sell their products, and the selling process takes place over multiple channels, including in person, over the phone, and online. Because insurers face increasing pressure to produce better returns on their marketing investments, they are now using predictive analytics to analyze consumer behavior, which helps calculate their propensity to purchase specific products. Insurers can collect policyholders’ data over time to determine individual policyholder receptiveness to cross-selling other products and when it is appropriate. In doing so, they can see increases in conversion, cross-product, and retention ratios.
- Producer acquisition and value management. The average age of an insurance agent is 57. As retirements reduce the number of agents and advisors and economic growth remains low, acquiring, retaining, and enhancing producer productivity has become an even greater priority. Predictive modeling can combine internal insurer data with external socio-demographic data to determine the market potential for specific products. These insights can help the head office sales force improve producer acquisition, retention, and productivity ratios.
- Underwriting and pricing. Actuaries and underwriters have typically used predictive modeling to compute risk scores based on things like an individual’s socio-demographics, driving record and behavior, and credit score. They use these predictive scores to determine pricing, as well as automate the underwriting process by setting rates and automatically approving customers for coverage beyond that line. Similarly, they can automatically reject customers who fall below a certain threshold, leaving underwriters to manually evaluate a smaller set of customers. Auto insurers have long used such techniques, but now property, commercial, and life insurers are as well. Predictive modeling plays a critical role in reducing underwriting cycle time, enhancing the ease of business for agents, increasing underwriting consistency, and reducing underwriting costs, all of which lead to better risk pricing. The positive results for insurers include reduced expense ratios, better underwriting results, and enhanced customer and agent satisfaction.
I recommend his article for further insights on this topic.