Predictive insurance is an advanced type of analysis that allows insurance companies to make forecasts using their historical data, combining statistical models, data mining techniques, and machine learning. Insurance companies use predictive analytics to identify recurring patterns within the huge stream of data available to them and use these patterns to identify risks and develop opportunities.

In this post, we will see how predictive insurance enables dynamic customer engagement at different stages of the funnel, from onboarding processes to policy renewal. We will also discover why integrating predictive insurance into daily operations is now an essential and strategic move, the only one that can enable a customer experience that matches the increasingly high expectations of customers. 


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What is predictive insurance?

The practice of using predictive analytics in insurance is not new; in fact, insurance companies have relied on it for years. The key difference is that today, the analysis activity is no longer done manually but through artificial intelligence-based technologies that automate redundant and repetitive tasks and streamline and speed up traditionally time-consuming and human-error-prone processes.

Today, companies are inundated with data of various types—from log files and images to video. Gaining insights from this data and predicting future outcomes, trends, and behaviors can now be processed through artificial intelligence applications and machine learning algorithms. The information resulting from these processes enables companies to optimize their strategies so as to minimize risk and maximize profits

Predictive insurance is thus predictive analytics applied to the insurance industry, an extraordinarily effective tool that is used to process claims and detect fraud, to anticipate financial risks and optimize prices, to identify dropout risk situations, and develop dedicated proposals to convince dissatisfied or undecided customers to renew their policies and maintain coverage. 


The steps in the predictive insurance process

Predictive analytics in insurance involves the collection and analysis of large data sets from which useful insights can be extracted to predict the likelihood of damage, fraud, and risk of policy cancellation. For predictive analytics to provide effective support, a number of steps must be addressed:

1. Define objectives, data sets, metrics.

Before starting any analytics activity, even before data collection, it is essential to determine the objectives: from detecting fraud attempts, to optimizing rate plans and from developing upselling and cross-selling propositions to increasing customer engagement and activating self-service modes. At this stage, it’s crucial to define the dataset to be analyzed. The next step is to identify the most appropriate KPIs to measure the success of the different initiatives. Only through a selection of metrics—which must be appropriate to the objectives set—will it be possible to evaluate the results obtained and, if the predictive model adopted does not work, to be able to immediately intervene to modify it.


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2. Data collection: an indispensable support for InsurTech

For predictive insurance to produce increasingly accurate results, collecting large volumes of historical data is essential. Today, many insurance organizations collect information from many sources. It’s good practice to have a data lake, a centralized repository into which all data, both quantitative and qualitative and structured and unstructured, flow. The incredible support provided by artificial intelligence is evident here: data no longer has to be manually extracted. The insurance technology available today, Insurtech—an umbrella term that combines “insurance” and “technology” and refers to everything related to technological and digital innovation in the insurance industry—is able to automatically and autonomously collect data from the various sources (mobile applications, telematics, IoT, customer interactions, social media, etc.).

A key element of insurtech and a key step in data collection is dematerialization—in the sense of both digitizing paper documents and directly creating the digital document. By extracting data from digital documents, insurance companies can quickly acquire knowledge about their customers that is not only significantly greater than in the past, but can exceed this to reach previously unthinkable levels of granularity. In fact, these tools make it possible to subdivide customers into increasingly specific clusters based on homogeneous characteristics that can be chosen on a case-by-case basis, depending on the specific information requirements.

After collecting the data, there is one more task to be done before proceeding to the actual analysis: the relevance and quality of the data must be confirmed before entering them into a predictive analysis model. This basically means: checking their format, removing duplicate data points, and reviewing data types to correlate them with their sources.

3. Modeling and distribution

Once all the data has been collected and the hypothesis to be tested has been determined, we can proceed to modeling, in other words, selecting or creating the predictive analysis model. This is the stage where machine learning techniques are used. After the model has been created and tested, you can start deploying it by including it in a real software application (for example, in the claims management flow or in platforms for automatically personalizing policy plans).

4. Monitoring

Now, it’s a matter of recording and evaluating the performance of the predictive analytics model and whether it meets the expected performance and accuracy requirements when fully deployed. What do the KPIs show in the initial phase (that of goal setting)? Monitoring must be constant and timely: the model results  can change significantly, even during relatively short periods, and continuous monitoring is essential in order to identify if and when the predictive analytics system is no longer delivering relevant insights.


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The benefits of predictive analytics in the insurance industry

With more than two-thirds of insurers planning to increase investment in data collection and analysis in the coming years, the use of predictive analytics models will have a significant impact throughout the insurance industry. What are the benefits that are driving the increasing adoption of predictive insurance tools and methodologies?

1. Predictive insurance contributes to economic growth

For 67% of insurance companies, predictive analytics helped reduce expenses related to the issue and underwriting of a policy, while 60% reported increased sales and profitability. These two findings tell us that advanced data analytics helps minimize waste and increase the effective use of resources, including by employing existing technologies in innovative ways.

With predictive analytics, a company is able to secure targeted insurance plans, speed up claims processing, and offer more personalized customer experiences. This all creates a competitive advantage that can attract new customers and retain existing ones. 

Predictive insurance also plays a strategic role in identifying potential markets: quality data can be used to reveal the behavior patterns and common characteristics of the target audience and to discover new pockets of overlooked growth or unexplored segments.

2. Predictive insurance supports hyper-personalized experiences

Predictive analytics enables you to detect patterns of customer behavior and identify the ones who are dissatisfied and may not renew their policies. With the comprehensive and timely insights that result from data analysis, you can focus on the motivations of these policyholders and on creating experiences that can meet their preferences and needs.

By anticipating customers’ needs and behaviors, you can design even more personalized interactions and build lasting relationships. For example, predictive analytics is used to offer customized insurance plans based on claims history. Insurers must strive to personalize offerings at any point in the customer journey, from quoting, to underwriting, and beyond. However, to achieve these goals, personalization may no longer be enough.

According to Capgemini, “When it comes to the insurance industry, today’s priority is a well-defined hyper-personalization strategy that focuses on experience-based engagement: delivering the right products, at the right time, through the right channels.”

An effective hyper-personalization strategy has three basic aspects: in-depth understanding of the customer, use of new technologies, and utilizing a fully customer-centric marketing approach.

  • Customer understanding. Data comes from all the touch points where your customers interact with the company: website, mobile app, social media, contact center. The information collected must be stored securely and at the same time be easily accessible to the teams engaged on that particular project.
  • Employ technologies. Data must be collected, sorted, and cataloged in dedicated places such as CRMs or advanced platforms that integrate different functionalities: from CCMs that enable efficient communication to customers on different touchpoints using all available channels, to products that enable interactive experiences.
  • Personalized marketing. The use of customer data from various channels within different marketing gives an insurance company the tools to build highly personalized relationships, which are more likely to result in higher levels of retention. Insurance marketers must therefore enhance this ability to translate information from a variety of sources into immediately actionable knowledge.

Predictive analytics can become a key element within a hyper-personalization strategy: it can be the initial moment in a process that transforms data into valuable relationships, improves the customer experience for policyholders, and creates a competitive advantage for companies.

3. Predictive insurance enables dynamic customer engagement

Through predictive insurance, especially when enhanced by artificial intelligence, insurance companies can design dynamic customer journeys: AI-based chatbots, “predictive routing” to identify the best agent for a specific customer, personalization of communication strategy based on customer data (such as sending personalized offers to retain at-risk customers). Predictive analytics can turn data into usable and immediately actionable information at the most sensitive moments in the funnel, on which the completion of business transactions between insurance companies and consumers depends: policy renewal and onboarding.

  • Policy renewal. This is the moment when clear and engaging messages are more important than ever. It requires a solution that is both innovative and effective so as to enrich the communication, making it a highly relevant, interactive experience. Personalized videos, which translate the results of predictive analytics into storytelling with images, is the type of content that is best suited to achieve a consistent, clear, and engaging type of communication for each customer.
  • Onboarding. When it comes to onboarding a new customer, every opportunity of contact is valuable because it is potentially unrepeatable. Even in the insurance industry. Digital tools today make it possible to automatically upload profile data to internal platforms. From there, through predictive analytics, this data will go on to form the knowledge base from which insights can be extracted to build experiences that are increasingly centered on the specific needs of policyholders (potential and acquired). Also crucial at this stage are all the solutions that offer organizations complete coverage of digital processes related to fiscal and document-based processes and enable their seamless integration with the tools and procedures already in use (including the essential functionality of electronic signatures).

Having clarified what predictive analytics is and how it works in the insurance industry and having highlighted the countless benefits it offers, we can draw some conclusions. For example, we can say that the knowledge produced through predictive insurance techniques and tools provides a solid foundation for developing more centered business proposals. Thanks to this, companies are able to easily connect with new customers and maintain valuable relationships with existing customers, providing them with highly personalized services, from communications regarding when and how to pay their policies, to customer care processes.