Three challenges to effective data analytics use in insurance
By actively overcoming barriers, the industry will be better prepared to embark on the next frontier of data and analytics.
To stay competitive in a data-rich world, insurance companies are amassing vast amounts of data with the intent of optimizing performance, mitigating risk and meeting rising consumer expectations.
While companies may be aggregating more data, they also are facing challenges that may prevent them from harnessing the true power of analytics.
TransUnion recently commissioned Aite Group to conduct a study of insurance and financial services professionals to gain a better understanding of how companies are adapting to data and analytics, and how they are playing a more prominent role within these industries.
The study found that insurance professionals, in particular, have a set of challenges that are unique to the industry. Fractured data and legacy systems, managing data and analytics at the product level, and a lack of talent and tools are just a few of the challenges that we will explore.
Challenge 1: Fractured data and legacy systems prevent insurance companies from extracting value and making the data actionable.
The insurance business is established on the law of big numbers. The industry uses data to inform decision-making and manage growth and profitability across marketing, underwriting, pricing and policy servicing processes. However, like most established financial institutions, insurance companies have various data repositories and different teams managing analytics functions — and are traditionally not good at sharing this information or communicating with one another.
Many of the insurance professionals included in the study reflected these sentiments. They described a “standard business practice” as one where each business unit has its own process for capturing data. As a result, definitions of key terms may not be consistent, creating barriers toward seamless integration. Therefore, it is not surprising that the majority of respondents (60%) said they employ a hybrid approach of building and buying their analytical solutions. This causes significant organizational inefficiencies and prevents insurance companies from realizing the full potential of data and analytics.
Professionals understand the importance of maintaining a competitive edge, as 70% of those surveyed indicated that a single platform, one that coordinates and connects internal and third-party systems, is a major differentiator. However, only about two in 10 respondents indicated that their current solutions have these capabilities. This highlights the need for coherent enterprise data and analytics strategy and a common platform to hold and integrate existing and new data sources, as well as analytical tools. The platform needs to be flexible in order to support different skill sets, react to changing market conditions and have the ability to integrate alternative sources of data.
Challenge 2: Data and analytics are managed at the product level rather than at the customer level, making it difficult to create a comprehensive view of the customer.
More often than not, each product within an insurance company has its own process for how to capture, manage and use data about customers. Customer insights are isolated to silos and scattered across lines of business, functional areas and even channels. As a result, much of the work that surrounds the handling of data becomes somewhat janitorial. With no common keys or even set definitions of key terms (such as “customer”), 40-50% of an analyst’s time is spent wrangling the data.
This can lead to challenges, such as being unable to recognize the same customer across product lines and/or at different stages of the policy life-cycle. Direct and agency channels may compete for the same customer or attract a high-risk prospect that was turned down previously by underwriting. Since the claims department data is not available to pricing and marketing to inform their decisions, the result is often extra expenditures and a larger than necessary marketing budget that could easily be streamlined should these inefficiencies be addressed.
By managing data at the product level, it is difficult for insurance companies to understand all of the mechanisms surrounding customer value. There is, however, a significant demand for customer-centric analytical solutions, which allow insurance companies to link different pieces of data about a customer, creating a holistic view across product lines and throughout the policy lifecycle. Customer-centric solutions will help insurance companies realize important business goals, including more accurate targeting, longer retention and better profitability.
Challenge 3: Insurance companies recognize the need for new data sources, but do not have the necessary tools or talent in place to process and analyze the data.
Many analytical executives no longer view gaining access and capturing data as a barrier. More than half of the study’s respondents plan to increase spending on most types of data sources, especially newer ones, such as mobile. However, as the number and types of data sources grow exponentially and big data gets even bigger, it becomes increasingly difficult for analytics executives to find valuable insights.
Leveraging the right tools and talents to process and analyze data remains a key challenge for executives. Nearly half (45%) of insurance professionals indicate that having the right talent greatly improves their ability to underwrite profitable policies. However, about the same amount (42%) indicate that it is challenging to find qualified data scientists. Prepping the data is often where the real heavy lifting is done, as a significant amount of time is spent in data cleansing and preparation. This prevents analytical teams from performing more value-added activities, such as model development.
Addressing the challenges that arise from big data volumes generated by IoT applications and other new technologies requires an enterprise data management strategy. This is important when merging new and traditional data, such as customer and policy records.
The use of descriptive, prescription and predictive analytics is also gaining popularity, with over 40% of respondents stating they are interested in leveraging more advanced techniques, such as AI, in the next 24 months.
While the insurance industry faces a plethora of challenges with data and analytics, it’s imperative that executives are recognizing these challenges and are beginning to explore how to address them. By overcoming these barriers, the industry will be better prepared to embark on the next frontier of data and analytics.
Yuan Rao (yrao@transunion.com) is vice president of data science and analytics at TransUnion.
*For more information about TransUnion/Aite Group study, please visit the “Drowning in Data: Thirsty for Insights” landing page.
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