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Insurance companies have data as their main driving force and beating heart. With advancements in technology, colossal volumes of information lay in the hands of insurance businesses across the globe, and advanced analytics helps them in optimizing performance with predictive analytics, enhancing customer experience, improving risk assessments, reducing costs in claiming processes, and ensuring sustainable business growth. Data analytics in the insurance sector continually evolves, and answering to the ever-changing demands with the help of modern tools and software such as datapine’s online BI software will ensure greater agility and possibility to survive in our competitive business environment.

"We were amazed how easy it was to use datapine and get the first KPIs within a couple of minutes. An average Excel user can configure and work with datapine with zero IT overhead."

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The insurance industry heavily relies on data and benefits from connecting analytics with business processes, using computing technologies to achieve better performance, and upgrading underwriting procedures to offer more effective pricing and risk assessments.

  • Perform detailed cost analysis to improve risk management
  • Simplify data collection processes and access information in real-time
  • Offer better insurance plans and avoid human-based errors
  • Estimate future data behaviors with the help of predictive analytics
  • Optimize customer analytics processes and gain a competitive advantage

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Insurance analytics is the process of collecting, analyzing, and extracting relevant insights from various data sources to effectively manage risks and offer the best possible insurance contracts in fields such as health, life, property or casualty, among others. While there are companies, agents, managers, and professionals that offer one or multiple insurance services, the main goal of each of them is to reduce costs, optimize customer relationship processes, and use predictive analytics in insurance models to create reliable reports.

Insurance analytics tools and solutions help brokers and companies to efficiently track expenses, ensure stable financial operations and liquidity, and monitor numerous operational expenses such as sales, marketing, operations, or customer support. Developing insurance policies is no longer a tedious task since analytics help in optimizing operations, help individuals to make more informed decisions, and act as a connector between numbers and humans.

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Why is data analytics in the insurance sector so important?

insurance analytics KPI example

The rise of self-service analytics tools has enabled the industry to give the power of data management processes to everyone in the company, from sales managers responsible for selling policies, to the financial department that needs a clear overview of expenses, profits, and other relevant financial data. Consolidating data from multiple sources, and tinkering important metrics such as the costs of claims, customer satisfaction rates, sales revenue, or net income, have all been scattered through numerous departments and hardly connected to the overall business picture. By striking the balance between insurance data & analytics, companies have a chance to reap great rewards, gain an advantage over competitors, and increase profits in the long run.

In recent years, there are many more talks about predictive analytics in the insurance industry, and professional business intelligence solutions such as datapine provide a simple means of forecasting the future behavior of data and reporting with just a few clicks. We will focus on predictive models later, but now it’s important to keep in mind that machine learning in insurance has enabled a more effective collection of data, and user-friendly solutions that give insurance companies a detailed outlook on information that could, otherwise, stay hidden in spreadsheets.


There are numerous applications of business analytics in the insurance industry, from predictive analytics, customer and claims analytics, marketing, sales and, in essence, across all areas of an organization. Here we will list the most important ones:

Insurance risk analytics: Risk management is one of the building blocks of a successful insurance company. Insurers collect and track massive volumes of data and need to develop high or low-level risk profiles in order to determine the values of policies they offer. That’s where advanced statistics and analytical processes have the deepest impact as data mining techniques, e.g., can serve as tools to determine the risk levels of a policy. It’s especially notable in the underwriting processes, to enhance this important part of insurance data analytics.

Insurance claims analysis: When analyzing claims, metrics such as the average cost per claim, frequency, claims ratio, or time to settle a claim will provide users a detailed outlook into how much should be paid out, what kind of claims’ loss likelihood is present, and where are anomalies in the overall business performance. When conducting the analysis with modern insurance analytics software, each stage of the process can be augmented with intelligent alarms that will notify the insurer if an anomaly occurs. That way, human errors in calculations are minimized and claims monitored in a more detailed, effective way.

Customer analytics: Customer experience is critical in the insurance industry. From customer retention and loyalty to predicting the behavior and launching tailor-made offers. Here it’s not just important to focus on analyzing customer calls, for example, but also to connect with marketing and deliver messages at the right place and time, which will, in turn, lead to excellent customer satisfaction levels. Insurance customer analytics brings together multiple touchpoints and professional insurance BI tools help in connecting various dots in the customer journey.

Health insurance analytics: This point deserves special attention since, in healthcare, there are numerous factors that affect the policies and claims processes. A data-driven approach can ease the processes of care management such as monitoring the number of members that have used the insurance for their health issues, and developing customized health insurance plans for patients that have issues with chronic diseases, just to name a couple of use cases. Moreover, claims management backed by modern business intelligence ensures greater efficiency since automation can play a big role in optimizing analytical processes.

Finally, predictive analytics in the insurance industry: As we already mentioned, predictive analytics have transformed the insurance sector, and it deserves a special mention since it’s closely connected to each step of the insurance way. From pricing policies, risk management, predicting future behaviors, churn prevention, marketing activities and sales, forecasting models are becoming the norm in the industry. With the help of modern business intelligence solutions for insurance companies, predicting the future is easier than ever before. Insurers can simply insert specified data points into the tool and the software will calculate predictions on sales revenue for the next 6 months, for example. That way, companies can plan more effectively and estimate what kind of resources they will need to achieve business goals. There are various insurance predictive analytics examples that accompany these notions such as pricing and product optimization. In essence, an insurance company can predict risk and price more accurately by monitoring the claim history in the property insurance sector, construction costs, or weather patterns.

These are just some examples of how would the insurance industry use business intelligence and it shows the applications are widespread, adjustable to each use case, and empowered with advanced technologies. At datapine, we are certain that insurance data analytics software takes information management a step further – increasing operational efficiency, helping in ad hoc and strategic analysis, connecting the dots between departments, and providing a centralized place for multiple business touchpoints. We are focused on providing support on every step of your analytical journey, and use intelligence as the backbone for your insurance company, providing you with a platform that will generate business value, reduce your operational costs, increase the productivity of the whole team, and relieve you from tedious manual tasks.


insurance analytics metric example

By implementing state-of-the-art solutions such as BI in insurance, companies can create KPI dashboards, reports, utilize effective data discovery, and data mining processes that were traditionally restricted for advanced analysts and IT departments. But with recent technological expansions, these tools have emerged to provide everyone in the insurance industry the possibility to self-serve every analytical process they might need such as the claims management processes, the ability to forecast revenue and optimize sales management processes to improve performance, or to identify market trends in order to gain strategic advantage by reviewing products and services within their catalog, and optimize for market demands.

Business intelligence for insurance gives the data power and enables users to keep continuous track of claims policies around health, property, weather, vehicles, accidents, etc. Hundreds of data points are easily connected into a single, centralized point of access with an insurance dashboard tool such as datapine. But not only, as advanced analytical capabilities will provide a seamless experience even for the most demanding users. On the other hand, average business users will have the opportunity to work with automation, a simple drag-and-drop interface, and take their analysis onto the next level.


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