By Nathan Root, Vice President, Insights and Date, Insurance. Capgemini
Two core elements of insurance value-chain, actuarial (risk pricing) and underwriting (risk assessment) have always leveraged some form of analytics. Insurance companies collect vast volumes of data from large cohorts and analyzing them to better understand and evaluate risks. The insights then feed into the actuarial models to price the risk faced by a large number of people or cohorts, in line with the principle of law of large numbers.
So what has changed now?
Technology is swiftly advancing the industry. With the advent of digital concepts, real time data is available, analytics are more sophisticated, and the speed from insight to action is faster. Today, data accessibility is growing at a faster pace than ever before due to online digital sources such as telematics, wearable devices, and social media. FinTechs are advancing rapidly by developing and deploying big data analytics models and frameworks, such as Hadoop, MongoDB and Amazon Redshift, to quickly analyze large volumes of data from various sources. Modern digital platforms allow for rapid changes in products to meet rapidly changing customer needs
Moreover, customer preference and engagement metrics are evolving rapidly through enhanced technology players. Today’s customers, especially tech-savvy Gen Y individuals, have a strong preference for greater convenience, personalization, and transparency expecting more choices. As well as this, digital era customers expect insurance companies to engage with them in a manner they prefer.
While the entire insurance world has been using some form of analytics tools for actuarial and underwriting decisions, the time is right for insurance companies to move beyond basic analytics tools, such as relational databases, credit scoring, and rule-based engines. Increasing availability of real-time data and growing need for real-time and actionable insights make deployment of advanced analytics tools critical.
Currently, most insurers don’t have a wholesome framework to leverage analytics across the value-chain. Insurers need to redesign an operating model keeping digital concepts at the center and using advanced data and analytics across the enterprise – from product design to claim payments. The industry should look to gradually move from analyzing past data to predicting future patterns by leveraging advanced tools such as business intelligence, predictive models, artificial intelligence and cognitive computing.
For example, the insurance industry needs to move from customer intelligence to more sophisticated behavioral analytics.
Behavioral analytics not only evaluates customer preferences and shopping patterns, but analyzes significant variables to help companies predict future needs of customers. Hence, adoption of an advanced, predictive analytics tool becomes critical for insurers to create personalized offerings that cater to the future needs of a particular customer.
So the question is, by collecting more granular and personalized data in real time—and by enabling insurers to develop more customized offerings—is the reliance on the law of large numbers sufficient?
Simply put, the answer is no. To get meaningful insights from any data and analytics tools, however advanced it may be, insurers will still need to gather massive volumes of data about customers, risks and perils.
Since the credibility of insights generated through analytic tools increases with the size of the data pool, insurance companies today need to assess even higher volumes data than ever before in order to develop a personalized service by unpooling risk and unbundling perils.
It brings us back to the same point—insurance is an industry that operates on the principle of law of large numbers.
To remain competitive going forward, there are a few key questions the industry must ponder:
• Are insurers tapping into all data sources and collecting all available customer data?
• Are insurance companies completely and effectively utilizing the huge amount of data that they already have to carry out a more precise and personalized risk assessment?
• Have insurance companies developed sophisticated analytical capabilities to generate real-time insights from real-time data?
• Are insurance companies able to turn the insights from analytics and data into actions through modern platforms?
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