A survey of life insurers in December 2016 by the UK-based Willis Towers Watson indicates eight percent life insurers are actually using Big Data in their decision-making. While sixty-two percent life insurers plan to use predictive analytics and Big Data soon for their business decisions. Big Data insights can play revolutionary changes in Life Insurance if the execution of technologies and techniques for implementing predictive analytics planned properly. Besides, the potential of Big Data is immense for life insurers, as it can improve their efficiency in managing their enterprise and serving their customers.
But, life insurers need to address many challenges and barriers to realize the potential of Big Data, its application, and predictive analytics. The biggest is infrastructure limitations. Other limitations can include financial constraints, expertise, lacking knowledge, conflicting priorities, data availability, workforce, skills, resources, training, and capabilities. Above all, they need commitment.
The life insurers planning the use of Big Data need to examine what information to collect, from which sources to collect, where and when to collect, where to store and how diligently to use it for deriving insights. The data sources can include details of policyholders, agents, claims, emails, administrative systems, website visitors, medical records, social media, and credit scores. Possibly future can throw more data sources both internal and external. All these needs right analytics to understand insights capable of helping the improvement in the administration of life policies, building innovative and bold policies, preventing fraudulent claims and improving customer relations. With predictive analytics and Big data, life insurers can improve their performance management, customer relations, enhance customer value propositions, and metamorphose their business model.
Big Data leverages efficiency, accuracy, and value in customer engagement meaningfully. This enables offering tailored services and products to suit their needs in a superior way. It also provides a chance for life insurers to improve their processes and business models for a collaborative mutual or reciprocal relation with policyholders and other customers. This paves the way to innovate and revolutionize from within to entirely a new dynamic transformation. Further, they can find ways to minimize customer complaints, meet customer needs by tailoring products and improving risk mitigation. It is for the insurer to realize this opportunity to leverage Big Data by building more mutually beneficial and meaningful customer relations. Innovation is the key to leverage Big Data for adopting revamped and innovative products to attract more customers. They can learn about customers with a new perspective and structure superior products and services with a dynamic pricing approach. In addition, insurers can also use health data for effectively targeting particular segments of customers to develop more personalized insurance premiums.
It is but natural to manage and keep personal information confidential rather than managed by a third party or any organization. Not only has that, customers tended to hesitate to share their information with organizations. This provides a need for instilling confidence by improved collaboration between customers and life insurers. Here imagination is the limitation for discovering such fruitful relations between both insurers and customers. For example, the insurer can incentivize customers for managing their own policies and health, by providing gym discounts, diagnostic discounts and so on. By this extended customer experience, insurers can gather customer data in a real time, and continuously rather than getting it at the underwriting time or after filing a claim. Capturing data continuously facilitates streamlined underwriting and hassle free claim processing, eliminating waiting time for customers and other stakeholders for information access. Big Data can accelerate self-servicing expectation to meet customers’ needs, giving them power for accessing and sharing information.
On the other hand, with Big Data life insurers can identify groups of customers, such as similar age groups, so that they can offer them services and targeted insurance products. Life insurers can also ascertain the expectations of the customers to know what influences them to buy an insurance product. Such as weddings, births, or an important life event or a naturally occurring disaster can influence their insurance buying decision. Life insurers also need to allay the fears of customers to make them comfortable about the safety and security of their information, using it and storing it. Linking data already used by other providers such as vehicle insurance or a health insurance to models of life insurance using Big Data can be one way to share customer information. Life insurers need ways to demonstrate and communicate with customers the benefits it can bring to them. They need to shift from doing their business the traditional way in proactively engaging the customers with their willing involvement. This can allow to better understand their customers, knowing their customers more, risk undertaking, claim predictions and improve their process relating to loss adjustment, recoveries, and fault determination.
Big Data can aid to restructure life insurance policies with superior value propositions and possible drop-offs in policy lapses. The insurers can have a full customer view and his risk potential by incorporating his other available policy information such as a vehicle or a health insurance. Big Data provides continuous customer behavior pattern in his life cycle, instead of at a particular time. With this, life insurers can identify customer types and groups, and need not depend on customers for providing information by them. This enables them to develop custom-built policies to serve customer needs, besides streamlining the claims process and underwriting process. Big Data also helps identify future possibilities of policy lapses or claims from the customers. It streamlines key performance parameters or identifies automating processes or making priorities for necessary investments with data-driven decisions. It can transform fundamental relationships between customers and insurers.
Life insurers, however, also need to overcome barriers in data accessing and sharing information on restricted products, and controls by regulators to use Big Data in their insurance models. They need to access data from peers and regulators to refine the product and its price. Terms and premiums are governed by regulators that can become a barrier for life insurers with fewer chances for product adaptation to the ever-changing needs of customers.