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Analytics has been the top strategic priority for insurance CIOs for many years according to Gartner Research. However other Gartner studies have found that the level of maturity among insurers around analytics is often low. This is problematic as the business demands grow and the need to innovate through intelligence becomes a strategic differentiator. In 2018, insurers will need to respond to four major trends to gain the analytics maturity needed to compete in the digital insurance marketplace.
First, insurers will need to apply new data sources to generate maximum results from analytics investments. Today most insurers rely almost exclusively on structured data, especially transaction data which originates from core systems. Strategic use of analytics will require greater use of unstructured data including text, voice, video, and images. Incorporation of these data types into advanced analytics will be a valuable asset for differentiation in 2018, and help provide greater accuracy in all types of analytics from customer, underwriting and risk assessment.
Second, the roll out of IoT programs will provide the foundation for new types of analytics especially for behavioral economics modeling. Behavioral analytics is the future of the industry, but underutilized today. By using IoT sensor data, insurers can understand performance of the item that they are insuring (e.g., a car, vessel or home) and risk behavior of the policyholder. This will allow for greater contextualization of risk, the execution of prevention programs, improved risk analysis and real-time input for modeling such as claims or fraud. IoT will provide new intelligence unmatched in other analytical capabilities, driving precision and accuracy throughout the entire value chain.
Third, analytics strength and rigor must move beyond the actuarial department into other business units and functions such as claims, fraud and marketing. Today insurers’ strength is in pricing analytics, which has served as their competitive advantage for many years. Today’s environment, however, requires that insurers build the same analytical strength in algorithms like next best action, churn, fraud and underwriting risk, using new sources of data and algorithms which can be operationalized in real-time and embedded into core systems. This will help optimize data throughout the enterprise, driving greater ROI where data has been underutilized in the past.
Fourth and last, insurers must establish a technical arsenal using the latest technologies that will enable data science and decision optimization through machine learning and/or artificial intelligence. Big data and advanced analytics are foundational, but companies should seek to test and pilot new technologies that will help them with learning and cognitive analysis, such as personalizing customer experiences based upon life event information, automating customer interaction through chatbots, and supporting touchless claims handling for real-time claims handling using a series of automated algorithms. Learning-based systems will help insurers achieve maximum value from their data initiatives and help them build more precise models that match market trends, adjusting through real-life learning methodologies. This will propel insurers from being data “smart” to being able to run intelligent operations that evolve over time.
As demand for information innovation to enable digital insurance models grows, analytics initiatives will be the foundation of success. To successfully execute such initiatives, companies must attain higher levels of maturity. Insurers will need to shift from being data “smart” to being able to run intelligent operations that evolve over time and support data-driven decisioning throughout the entire value chain.
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